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Papers by Zahid A Ansari

Intelligent Data Analysis, Jun 29, 2017

Mining web usage data of e-business organizations is essential to provide knowledge about clients... more Mining web usage data of e-business organizations is essential to provide knowledge about clients' web utilization patterns, which can help these businesses in landing at vital business choices. Because of non-deterministic web access behavior of web clients, web user session data is usually noisy and imperfect. Such imperfection has a negative impact on pattern discovery process. One of the real issues associated with the prevalently used Fuzzy c-Means (FCM) and Fuzzy c-Medoids (FCMdd) methods is that they are not robust against the noise, because a single outlier object could lead to a very different clustering result. In this research we propose a robust Fuzzy c-Least Medians (FCLMdn) clustering framework to deal with the user session data contaminated with noise and outlier user session objects, with the objective of improving the quality of the extracted patterns. To deal with the high dimensionality of user session data which may contain noise and outliers, a fuzzy set theoretic approach for assigning fuzzy weights to user sessions and associated URLs has been proposed. Our results clearly indicate that quality of user session clusters formed using FCLMdn algorithm is much better than those using FCM and FCMdd algorithms in terms of various cluster validity indices.

Text classification, which aims to assign a document to one or more categories based on its conte... more Text classification, which aims to assign a document to one or more categories based on its content, is a fundamental task for Web and/or document data mining applications. In natural language processing and information extraction fields Text classification is emerging as an important part, were we can use this approach to discover useful information from large database. These approaches allow individuals to construct classifiers that have relevance for a variety of domains. Existing algorithms such as Svm Light have less GUI support and take more time to perform classification task. In this presented work classification of multi-domain documents is performed by using weka-LibSVM classifier. Here to transform collected training set and test set documents into term-document matrix (TDM), the vector space model is used. In classifier TDM is used to generate predicted results. The results emerged from weka with its GUI support using TDM have quick response time in classifying the documents.

Computational & Applied Mathematics, Jul 15, 2020

The heat transfer analysis coupled with fluid flow is important in many real-world application ar... more The heat transfer analysis coupled with fluid flow is important in many real-world application areas varying from micro-channels to spacecraft's. Numerical prediction of thermal and fluid flow situation has become very common method using any computational fluid dynamics software or by developing in-house codes. One of the major issues pertinent to numerical analysis lies with immense computational time required for repeated analysis. In this article, technique applied for parallelization of in-house developed generic code using CUDA and OpenMP paradigm is discussed. The parallelized finite-volume method (FVM)-based code for analysis of various problems is analyzed for different boundary conditions. Two GPUs (graphical processing units) are used for parallel execution. Out of four functions in the code (U, V , P, and T), only P function is parallelized using CUDA as it consumes 91% of computational time and the rest functions are parallelized using OpenMP. Parallel performance analysis is carried out for 400, 625, and 900 threads launched from host for parallel execution. Improvement in speedup using CUDA compared with speedup using complete OpenMP parallelization on different computing machines is also provided. Parallel efficiency of the FVM code for different grid size, Reynolds number, internal flow, and external flow is also carried out. It is found that the GPU provides immense speedup and outperforms OpenMP largely. Parallel execution on GPU gives results in a quite acceptable amount of time. The parallel efficiency is found to be close to 90% in internal flow and 10% for external flow.

Arabian journal for science and engineering, May 28, 2020

Conjugate heat transfer and fluid flow is a common phenomenon occurring in parallel plate channel... more Conjugate heat transfer and fluid flow is a common phenomenon occurring in parallel plate channels. Finite volume method (FVM) formulation-based semi-implicit pressure linked equations algorithm is a common technique to solve the Navier-Stokes equation for fluid flow simulation in such phenomena, which is computationally expensive. In this article, an indigenous FVM code is developed for numerical analysis of conjugate heat transfer and fluid flow, considering different problems. The computational time spent by the code is found to be around 90% of total execution time in solving the pressure (P) correction equation. The remaining time is spent on U, V velocity, and temperature (T) functions, which use tri-diagonal matrix algorithm. To carry out the numerical analysis faster, the developed FVM code is parallelized using OpenMP paradigm. All the functions of the code (U, V, T, and P) are parallelized using OpenMP, and the parallel performance is analyzed for different fluid flow, grid size, and boundary conditions. Using nested and without nested OpenMP parallelization, analysis is done on different computing machines having different configurations. From the complete analysis, it is observed that flow Reynolds number (Re) has a significant impact on the sequential execution time of the FVM code but has a negligible role in effecting speedup and parallel efficiency. OpenMP parallelization of the FVM code provides a maximum speedup of up to 1.5 for considered conditions.

Archives of Computational Methods in Engineering, Jan 13, 2016

Computational fluid dynamics (CFD) is one of the most emerging fields of fluid mechanics used to ... more Computational fluid dynamics (CFD) is one of the most emerging fields of fluid mechanics used to analyze fluid flow situation. This analysis is based on simulations carried out on computing machines. For complex configurations, the grid points are so large that the computational time required to obtain the results are very high. Parallel computing is adopted to reduce the computational time of CFD by utilizing the available resource of computing. Parallel computing tools like OpenMP, MPI, CUDA, combination of these and few others are used to achieve parallelization of CFD software. This article provides a comprehensive state of the art review of important CFD areas and parallelization strategies for the related software. Issues related to the computational time complexities and parallelization of CFD software are highlighted. Benefits and issues of using various parallel computing tools for parallelization of CFD software are briefed. Open areas of CFD where parallelization is not much attempted are identified and parallel computing tools which can be useful for parallelization of CFD software are spotlighted. Few suggestions for future work in parallel computing of CFD software are also provided.

International Journal of Computer Trends and Technology, Sep 25, 2015

The advent of high performance computing (HPC) and graphics processing units (GPU), present an en... more The advent of high performance computing (HPC) and graphics processing units (GPU), present an enormous computation resource for large data transactions (big data) that require parallel processing for robust and prompt data analysis. In this paper, we take an overview of four parallel programming models, OpenMP, CUDA, MapReduce, and MPI. The goal is to explore literature on the subject and provide a high level view of the features presented in the programming models to assist high performance users with a concise understanding of parallel programming concepts.

Design Engineering, May 21, 2021

Computational fluid dynamics (CFD) is one of the most emerging fields of fluid mechanics used to ... more Computational fluid dynamics (CFD) is one of the most emerging fields of fluid mechanics used to analyze fluid flow situation. This analysis is based on simulations carried out on computing machines. For complex configurations, the grid points are so large that the computational time required to obtain the results are very high. Parallel computing is adopted to reduce the computational time of CFD by utilizing the available resource of computing. Parallel computing tools like OpenMP, MPI, CUDA, combination of these and few others are used to achieve parallelization of CFD software. This book provides a comprehensive state of the art review of important CFD areas and parallelization strategies for the related software. Issues related to computational time complexities and parallelization of CFD software are highlighted. Benefits and issues of using parallel computing tools for parallelization of CFD software are briefed. Open areas of CFD where parallelization is not much attempted are identified and parallel computing tools which can be useful for parallelization of CFD software are spotlighted. Suggestions for future work in parallel computing of CFD software are also provided.

International Journal of Computer Trends and Technology, Sep 25, 2015

text classification is the task of automatically categorizing collections of electronic textual d... more text classification is the task of automatically categorizing collections of electronic textual documents into their predefined classes, based on their contents. Due to the increase in the amount of text data in these recent years, document classification has emerged in the form of text classification systems. They have been widely implemented in a large number of applications such as spam filtering, emails, knowledge repositories and ontology mapping. The main essence is to propose a text classification technique based on the feature selection and reduction of the feature vector dimensionality and increase the classification accuracy using pre-processing. This paper gives the detailed study on how support vector machine (SVM) can be used to classify uncertain data. SVM is a powerful and supervised learning sample based on the lowest structural risk principle. During training, this algorithm creates a hyperplane for separating positive and negative samples. The type of kernel used for SVM classifier will be having a major impact on classification results. In this paper Breast Cancer Wisconsin (Diagnostic) Data Sets are used in order to classify using four types of SVM kernel methods such as linear, polynomial, sigmoid and radial. Classification results obtained reveal that radial kernel method is best-suited data sets. In order to measure the suitability of kernel method, various factors are compared from classification results such as accuracy, kappa value, sensitivity, specificity precision etc.

International Journal of Computer Trends and Technology, Sep 25, 2015

Web usage mining is the application of data mining techniques to discover usage patterns from Web... more Web usage mining is the application of data mining techniques to discover usage patterns from Web data, in order to understand and better serve the needs of Web based applications. To analyze the pattern from large transactional database there are many algorithms. One of the algorithms which is very simple to use and easy to implement is the Apriori algorithm. But this apriori algorithm is time consuming algorithm during its candidate item- set generation. IP-Apriori i.e Improved Pruning in Apriori is the improved variation of Apriori algorithm which improves the pruning step of the existing apriori algorithm. This algorithm uses average support instead of minimum support in the pruning step, to generate the probabilistic item set instead of large item-set. This analysis work is on IP- Apriori algorithm on different datasets. Based on the comparison of frequent item sets generated and time consumed, its shown that IP-Apriori algorithm is better than the Apriori Algorithm.

Data mining and knowledge discovery in databases have been attracting a significant amount of res... more Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention. Data mining is the method of analyzing the large amounts of data stored in data warehouses. We can perform data analysis, classification, clustering etc. of huge data by using different algorithms. It is important to evaluate the performance of various clustering techniques because the application of different clustering techniques generally results in different sets of cluster formation. The performance can be evaluated in terms of accuracy and validity of the clusters, and also the time required to generate them, using appropriate performance measures. In this paper, we have analysed the performance of Self-Organizing neural network based clustering and k-Means clustering using Matrix Laboratory tool, MATLAB. These techniques are tested against the various datasets. Finally, their performance results are compared and presented. Keywords— Clustering...

Clustering is a fundamental task in data mining technique which puts more similar data objects in... more Clustering is a fundamental task in data mining technique which puts more similar data objects into one group and dissimilar objects into another group. The aim of this paper is to compare the quality of clusters produced by K-Means, Particle swarm optimization (PSO) and Fuzzy C-Means (FCM) for data clustering. The k-means algorithm is the most widely used partitional clustering algorithm technique in the industries and academia. The algorithm is simple and easy to implement. The main drawback of the K-Means algorithm is that it is sensitive to the selection of the initial cluster centers and it may converge to local optima. Fuzzy C-means algorithm is a popular algorithm in the field of fuzzy clustering. Fuzzy clustering using FCM can provide a data partition that is both better and more meaningful than hard clustering approaches. Particle Swarm Optimization (PSO) is an evolutionary computational technique which was motivated by the organism’s behavior such as schooling of fish and ...

Results in Physics, 2021

In this work, the partitioning clustering of COVID-19 data using c-Means (cM) and Fuzy c-Means (F... more In this work, the partitioning clustering of COVID-19 data using c-Means (cM) and Fuzy c-Means (Fc-M) algorithms is carried out. Based on the data available from January 2020 with respect to location, i.e., longitude and latitude of the globe, the confirmed daily cases, recoveries, and deaths are clustered. In the analysis, the maximum cluster size is treated as a variable and is varied from 5 to 50 in both algorithms to find out an optimum number. The performance and validity indices of the clusters formed are analyzed to assess the quality of clusters. The validity indices to understand all the COVID-19 clusters' quality are analysed based on the Zahid SC (Separation Compaction) index, Xie-Beni Index, Fukuyama–Sugeno Index, Validity function, PC (performance coefficient), and CE (entropy) indexes. The analysis results pointed out that five clusters were identified as a major centroid where the pandemic looks concentrated. Additionally, the observations revealed that mainly the pandemic is distributed easily at any global location, and there are several centroids of COVID-19, which primarily act as epicentres. However, the three main COVID-19 clusters identified are 1) cases with value <50,000, 2) cases with a value between 0.1 million to 2 million, and 3) cases above 2 million. These centroids are located in the US, Brazil, and India, where the rest of the small clusters of the pandemic look oriented. Furthermore, the Fc-M technique seems to provide a much better cluster than the c-M algorithm.

Archives of Computational Methods in Engineering, 2016

Computational fluid dynamics (CFD) plays an essential role to analyze fluid flows and heat transf... more Computational fluid dynamics (CFD) plays an essential role to analyze fluid flows and heat transfer situations by using numerical methods. Turbomachines involve internal and external fluid flow problems in compressors and turbines. CFD at present is one of the most important tools to design and analyze all types of turbomachinery. The main purpose of this paper is to review the state of the art work carried out in the field of turbomachinery using CFD. Literature review of research work pertaining to CFD analysis in turbines, compressors and centrifugal pumps are described. Various issues of CFD codes used in turbomachinery and its parallelization strategy adopted are highlighted. Furthermore, the prevailing merits and demerits of CFD in turbomachinery are provided. Open areas pertinent to CFD investigation in turbomachinery and CFD code parallelization are also described.

Archives of Computational Methods in Engineering, 2016

Intelligent Data Analysis, Jun 29, 2017

Computational & Applied Mathematics, Jul 15, 2020

Arabian journal for science and engineering, May 28, 2020

Archives of Computational Methods in Engineering, Jan 13, 2016

International Journal of Computer Trends and Technology, Sep 25, 2015

Design Engineering, May 21, 2021

Computational fluid dynamics (CFD) is one of the most emerging fields of fluid mechanics used to ... more Computational fluid dynamics (CFD) is one of the most emerging fields of fluid mechanics used to analyze fluid flow situation. This analysis is based on simulations carried out on computing machines. For complex configurations, the grid points are so large that the computational time required to obtain the results are very high. Parallel computing is adopted to reduce the computational time of CFD by utilizing the available resource of computing. Parallel computing tools like OpenMP, MPI, CUDA, combination of these and few others are used to achieve parallelization of CFD software. This book provides a comprehensive state of the art review of important CFD areas and parallelization strategies for the related software. Issues related to computational time complexities and parallelization of CFD software are highlighted. Benefits and issues of using parallel computing tools for parallelization of CFD software are briefed. Open areas of CFD where parallelization is not much attempted are identified and parallel computing tools which can be useful for parallelization of CFD software are spotlighted. Suggestions for future work in parallel computing of CFD software are also provided.

International Journal of Computer Trends and Technology, Sep 25, 2015

Results in Physics, 2021

Archives of Computational Methods in Engineering, 2016

Abstract Clustering data from web user sessions is extensively applied to extract customer usage ... more Abstract Clustering data from web user sessions is extensively applied to extract customer usage behavior to serve customized content to individual users. Due to the human involvement, web usage data usually contain noisy, incomplete and vague information. Neural networks have the capability to extract embedded knowledge in the form of user session clusters from the huge web usage data. Moreover, they provide
tolerance against imperfect and noisy data. Fuzzy sets are another popular tool utilized for handling uncertainty and vagueness hidden in the data. In this paper a fuzzy neural clustering network (FNCN) based framework is proposed that makes use of the fuzzy membership concept of fuzzy c-means (FCM) clustering and the learning rate of a
modified self-organizing map (MSOM) neural network model and tries to minimize the weighted sum of the squared error. FNCN is applied to cluster the users’ web access data extracted from the web logs of an educational institution’s proxy web server. The performance of FNCN is compared with FCM and MSOM based clustering methods using various validity indexes. Our results show that FNCN produces better quality of
clusters than FCM and MSOM.

The explosive growth of World Wide Web (WWW) has necessitated the development of Web personalizat... more The explosive growth of World Wide Web (WWW) has necessitated the development of Web personalization systems in order to understand the user preferences to dynamically serve customized content to individual users. To reveal information about user preferences from Web usage data, Web Usage Mining (WUM) techniques are extensively being applied to the Web log data. Clustering techniques are widely used in WUM to capture similar interests and trends among users accessing a Web site. Clustering aims to divide a data set into groups or clusters where inter-cluster similarities are minimized while the intra cluster similarities are maximized. This paper describes the discovery of user session clusters using the two most popular partition based clustering techniques namely k-Means and k-Medoids. These techniques are implemented and tested against the Web user navigational data. Performance and validity results of each technique are presented and compared.

Data mining is generally the process of examining data from different aspects and summarizing it ... more Data mining is generally the process of examining data from different aspects and summarizing it into valuable information. There are number of data mining software's for analysing the data. They allow users to examine the data from various angles, categorize it, and summarize the relationships identified.

Mining association rule is one of the key problems in data mining approach. Association rules dis... more Mining association rule is one of the key problems in data mining approach. Association rules discover the hidden relationships between various data items. In this paper, we propose a framework for the discovery of association rules using frequent pattern mining. We use preprocessing to transform the transaction dataset into a 2D matrix of 1's and 0's. Mining association rule must firstly discover frequent itemsets and then generate strong association rules from the frequent itemsets. The Apriori algorithm is the most well known association rule mining algorithm and is less efficient because they need to scan the database many times and store transaction ID in memory, so time and space overhead is very high. Especially they are less efficient when they process large scale database. Here we propose improved Apriori algorithm by including prune step and hash map data structure. The improved algorithm is more suitable for large scale database. Experimental results shows that computation times are reduced by using the prune step and hash map data structure.

Data clustering is an important analysis in data mining. Due to its important role, many clusteri... more Data clustering is an important analysis in data mining. Due to its important role, many clustering methods have been proposed. Unfortunately, most of them require predefined number of clusters. Thus in this paper, we tried to overcome this problem by performing automatic clustering using active clusters approach with particle swarm optimization heuristic method (ACACAPSO). We used the concept of active clusters for the clustering of data. Also, we used K-means method to update the cluster centroids.

Due to continuous proliferation of e-Commerce and Web information systems, Site owners facing int... more Due to continuous proliferation of e-Commerce and Web information systems, Site owners facing intense competition in attracting and retaining users. In today's highly competitive e-commerce environment, the success of the site depends on the sites ability to retain visitors and turn casual browsers into potential customers. Web servers of e-commerce sites accumulate huge volumes of user web activity logs. In this work, we have presented k-Means Clustering based approach for the discovery of web user session clusters. Since the web usage data usually involves imperfection and uncertainties we have also reviewed various Soft Computing techniques to deal with such data.Also the size of web usage data is usually very huge, we have briefly discussed few parallel computing options which may be utilized to enhance the web usage mining process.

Medical image mining is one of the most rewarding and challenging field of application in data mi... more Medical image mining is one of the most rewarding and challenging field of application in data mining and knowledge discovery. Soft computing is a consortium of methodologies that provides flexible information processing capability. Its aim is to exploit the tolerance for imprecision, uncertainty, approximate reasoning, and limited truth in order to achieve tractability, robustness, and low-cost solutions. Soft computing techniques such as fuzzy sets, neural networks, genetic algorithms, and rough sets are most widely applied for image mining. This paper presents a review on various papers on medical image mining using soft computing techniques and related issues were discussed and listed which can be resolved suitably using soft computing techniques.

Data mining is the method of analyzing the large amounts of data stored in data warehouses. Data ... more Data mining is the method of analyzing the large amounts of data stored in data warehouses. Data analysis is done by using various techniques such as clustering, classification, etc. These techniques include various algorithms. Since different algorithms results in different set of information, it is necessary to compare the performance of various algorithms. The performance can be analyzed based on accuracy and on various quality measures. In this paper, we analyzed the performance of two classification algorithms Neural Network based Pattern Recognition and k-Nearest Neighbor algorithm, using Matrix Laboratory tool, MATLAB. These algorithms are tested against the four different datasets. Their performance results are analyzed and presented.

There are many Local texture features each very in way they implement and each of the Algorithm t... more There are many Local texture features each very in way they implement and each of the Algorithm trying improve the performance. An attempt is made in this paper to represent a theoretically very simple and computationally effective approach for face recognition. In our implementation the face image is divided into 3x3 sub-regions from which the features are extracted using the Local Binary Pattern (LBP) over a window, fuzzy membership function and at the central pixel. The LBP features possess the texture discriminative property and their computational cost is very low. By utilising the information from LBP, membership function and central pixel, the limitations of traditional LBP is eliminated. The bench mark database like ORL and Sheffield Databases are used for the evaluation of proposed features with SVM classifier. For the proposed approach K-fold and ROC curves are obtained and results are compared.

The explosive growth of World Wide Web (WWW) has necessitated the development of Web personalizat... more The explosive growth of World Wide Web (WWW) has necessitated the development of Web personalization systems in order to understand the user preferences to dynamically serve customized content to individual users. To reveal information about user preferences from Web usage data, Web Usage Mining (WUM) techniques are extensively being applied to the Web log data. Clustering techniques are widely used in WUM to capture similar interests and trends among users accessing a Web site. Clustering aims to divide a data set into groups or clusters where inter-cluster similarities are minimized while the intra cluster similarities are maximized. This paper reviews four of the popularly used clustering techniques: k-Means, k-Medoids, Leader and DBSCAN. These techniques are implemented and tested against the Web user navigational data. Performance and validity results of each technique are presented and compared. (Abstract)

Analysis of web server logs of e-business organisations is critical to provide insight into users... more Analysis of web server logs of e-business organisations is critical to provide insight into users' web usage behaviour which can assist in designing most attractive websites. In this article, a mountain density function (MDF)-based fuzzy clustering framework to discover user session clusters from web logs is proposed. Major steps in this framework include web log preprocessing, MDF-based discovery of user session clusters and their validation. To deal with high dimensionality of user sessions, a fuzzy approach for assigning weights to user sessions has been proposed. For the discovery of user session clusters, fuzzy c-means (FCM) and fuzzy c-medoids (FCMed) algorithms are explored. Since the selection of suitable initial cluster centres is a big challenge, MDF-based fuzzy c-means (MDFCM) and fuzzy c-medoids (MDFCMed) algorithms are proposed to overcome this problem. Our results show that quality of clusters formed using MDFCM/MDFCMed is much better than FCM and FCMed.

Due to the continuous proliferation of e-businesses, there is intense competition among organizat... more Due to the continuous proliferation of e-businesses, there is intense competition among organizations to attract and retain customers. Analyses of the web server logs of these organizations are critical for obtaining insights into web usage behavior, which can support the design of more attractive web structures. In this study, we propose a mountain density function (MDF)-based fuzzy clustering framework for discovering user session clusters in web log data. The major steps in this framework include web log preprocessing, MDF-based discovery of fuzzy user session clusters, and validation of these clusters. To consider the high dimensionality of user session data, we propose a fuzzy approach for assigning weights to user sessions. Fuzzy c-means (FCM) and fuzzy c-medoids (FCMed) algorithms are used to cluster the user sessions. The selection of suitable initial cluster centers is a major challenge for these methods, so we propose MDF-based FCM (MDFCM) and FCMed (MDFCMed) algorithms to overcome this problem. MDF-based clustering is also used to estimate the number of clusters. Our results clearly indicate that the quality of the clusters formed using the proposed algorithms is much better in terms of various validity measures compared with the FCM and FCMed algorithms.

Clustering techniques are widely used in "Web Usage Mining" to capture similar interests and tren... more Clustering techniques are widely used in "Web Usage Mining" to capture similar interests and trends among users accessing a Web site. For this purpose, web access logs generated at a particular web site are preprocessed to discover the user navigational sessions. Clustering techniques are then applied to group the user session data into user session clusters, where intercluster similarities are minimized while the intra cluster similarities are maximized. Since the application of different clustering algorithms generally results in different sets of cluster formation, it is important to evaluate the performance of these methods in terms of accuracy and validity of the clusters, and also the time required to generate them, using appropriate performance measures. This paper describes various validity and accuracy measures including Dunn's Index, Davies Bouldin Index, C Index, Rand Index, Jaccard Index, Silhouette Index, Fowlkes Mallows and Sum of the Squared Error (SSE). We conducted the performance evaluation of the following clustering techniques: k-Means, k-Medoids, Leader, Single Link Agglomerative Hierarchical and DBSCAN. These techniques are implemented and tested against the Web user navigational data. Finally their performance results are presented and compared.

The World Wide Web continues to grow at an amazing rate in both the size and complexity of Web si... more The World Wide Web continues to grow at an amazing rate in both the size and complexity of Web sites and is well on it's way to being the main reservoir of information and data. Due to this increase in growth and complexity of WWW, web site publishers are facing increasing difficulty in attracting and retaining users. To design popular and attractive web sites publishers must understand their users' needs. Therefore analysing users' behaviour is an important part of web page design. Web Usage Mining (WUM) is the application of data mining techniques to web usage log repositories in order to discover the usage patterns that can be used to analyse the user's navigational behaviour. WUM contains three main steps: preprocessing, knowledge extraction and results analysis. The goal of the preprocessing stage in Web usage mining is to transform the raw web log data into a set of user profiles. Each such profile captures a sequence or a set of URLs representing a user session. This sessionized data can be used as the input for a variety of data mining. This paper presents data preprocessing activities of our web usage mining research project that aims at extracting and analysing the navigational behaviour of the users of a web site. In this paper we describe our methodology for data cleaning and preparation in order to identify unique users and user sessions. We have used two different kinds of time oriented heuristics T OH1and T OH2 to identify the user sessions. Our results show that application of T OH1 results in larger number of user sessions and most of them are accessing very small number of URLs. On the other hand T OH2 generates lesser number of user sessions and average number of URLs accessed per session are more than that of T OH1.

This paper provide a review of the available literature on data mining using soft computing. A cl... more This paper provide a review of the available literature on data mining using soft computing. A classification has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the favourite measure chosen by the representation. The usefulness of the different soft computing methodologies is highlighted. Usually fuzzy sets are appropriate for managing the issues associated to understandability of patterns; imperfect/noisy data, diverse media information and human interaction, and can provide estimated solutions quicker. Neural networks are nonparametric, vigorous, and reveal good knowledge and simplification capabilities in data-rich environment. Genetic algorithms offer disciplined search algorithms to choose a model, from diverse media data, based on some preference criterion/objective function. Rough sets are appropriate for handling dissimilar types of uncertainty in data. Various challenges to data mining and the purpose of soft computing methodologies are indicated.

The World Wide Web continues to grow at an amazing rate in both the size and complexity of Web si... more The World Wide Web continues to grow at an amazing rate in both the size and complexity of Web sites and is well on its way to being the main reservoir of information and data. Due to this increase in growth and complexity of WWW, web site publishers are facing increasing difficulty in attracting and retaining users.This explosive growth has necessitated the development of Web personalization systems in order to understand the user preferences to dynamically serve customized content to individual users. To reveal information about user preferences from Web usage data, Web Usage Mining techniques are extensively being applied to the Web log data. Clustering techniques are widely used in Web Usage Mining to capture similar interests and trends among users accessing a Web site. Clustering aims to divide a data set into groups or clusters where inter-cluster similarities are minimized while the intra cluster similarities are maximized. Neural Network based Kohonen clustering networks (KCNs) provide unsupervised learning schemes to find the most suitable set of weights for hard clusters in a sequential and iterative manner. In this paper we discuss the use of Kohonen Clustering Networks to discover the clusters of web usage sessions from the web navigational data. We implemented and tested the technique against the Web user sessions extracted from the web logs of an educational institution's Web server. We present the performance and validity indices of the discovered clusters and compare them with that of c-Means clustering results.

The explosive growth in the information available on the Web has necessitated the need for develo... more The explosive growth in the information available on the Web has necessitated the need for developing Web personalization systems that understand user preferences to dynamically serve customized content to individual users. Web server access logs contain substantial data about the accesses of users to a Web site. Hence, if properly exploited, the log data can reveal useful information about the navigational behaviour of users in a site. Web Usage Mining is the application of data mining techniques to web usage log repositories in order to discover the usage patterns that can be used to analyse the user's navigational behaviour. Web Usage Mining consists of three main steps: preprocessing, knowledge extraction and results analysis. During the preprocessing stage, raw web log data is transformed into a set of user profiles. Each user profile captures a set of URLs representing a user session. Clustering can be applied to this sessionized data in order to capture similar interests and trends among users navigational patterns. Since the sessionized data may contain thousands of user sessions and each user session may consist of hundreds of URL accesses, dimensionality reduction is achieved by eliminating the low support URLs. Very small sessions are also removed in order to filter out the noise from the data. But direct elimination of low support URLs and small sized sessions may results in loss of a significant amount of information especially when the count of low support URLs and small sessions is large. We propose a fuzzy solution to deal with this problem by assigning weights to URLs and user sessions based on a fuzzy membership function. After assigning the weights we apply a "Modified Mountain Clustering" algorithm to discover the clusters of user profiles. Our results show that fuzzy feature evaluation results in better performance and validity indices for the discovered clusters.

while wireless networks are growing in popularity, monitoring these networks for abuse and intrus... more while wireless networks are growing in popularity, monitoring these networks for abuse and intrusions is almost nonexistent. Although some Intrusion Prevention Systems have appeared on the market, their intrusion detection capabilities are limited. Real intrusion detection in wireless networks is not a simple add on. This paper discusses a methodology for intrusion detection in wireless network using a cumulative sum algorithm.

Web robots are software programs that run automated tasks over the internet. They pass through th... more Web robots are software programs that run automated tasks over the internet. They pass through the hyperlink structure of the World Wide Web so that they can retrieve information. There are many reasons to differentiate web robot requests and user requests. Some tasks of web robots can be harmful to the web. Firstly, Web robots are employed to assemble business intelligence at e-commerce sites. In such a state of affairs, the e-commerce site may need to detect robots. Secondly, many e-commerce sites carry out a Web traffic inspection to deduce the way their customers have accessed the site. Unfortunately, such scrutiny can be erroneous by the presence of Web robots. Thirdly, Web robots often consume considerable network bandwidth and server resources at the expense of other users. A web log file is a web server file automatically created and maintained by a web server to check the activity performed by it. It maintains a history of page requests on its site. In this paper, we have used four methods together to detect and finally confirm requests as a robot request. Experiments have been performed on the log file generated from the server of an operational web site named vtulife.com which contains data of march-2013. In our research results of web robot detection using various techniques has been implemented. In this work, the provision of selecting any of the methods, selecting all of the methods and selecting any combination of the desired methods are available. Also, we can compare and integrate different methods. Keywords-web usage mining, web robot detection, web log file.

Due to the continuous increase in growth and complexity of WWW, web site publishers are facing in... more Due to the continuous increase in growth and complexity of WWW, web site publishers are facing increasing difficulty in attracting and retaining users. In order to design attractive web sites, designers must understand their users' needs. Therefore analysing navigational behaviour of users is an important part of web page design. Web Usage Mining (WUM) is the application of data mining techniques to web usage data in order to discover the patterns that can be used to analyse the user's navigational behaviour. Preprocessing, knowledge extraction and results analysis are the three main steps of WUM. Due to large amount of irrelevant information present in the web logs, the original log file can not be directly used in the WUM process. During the preprocessing stage of WUM raw web log data is to transformed into a set of user profiles. Each user profile captures a set of URLs representing a user session. This sessionized data can be used as the input for a variety of data mining tasks such as clustering, association rule mining, sequence mining etc. If the data mining task at hand is clustering, the session files are filtered to remove very small sessions in order to eliminate the noise from the data. But direct removal of these small sized sessions may result in loss of a significant amount of information specially when the number of small sessions is large. We propose a "Fuzzy Set Theoretic" approach to deal with this problem. Instead of directly removing all the small sessions below a specified threshold, we assign weights to all the sessions using a "Fuzzy Membership Function" based on the number of URLs accessed by the sessions. After assigning the weights we apply a "Fuzzy c-Mean Clustering" algorithm to discover the clusters of user profiles. In this paper, we provide a detailed review of various techniques to preprocess the web log data including data fusion, data cleaning, user identification and session identification. We also describe our methodology to perform feature selection (or dimensionality reduction) and session weight assignment tasks. Finally we compare our soft computing based approach of session weight assignment with the traditional hard computing based approach of small session elimination.