Fokrul Alom Mazarbhuiya - Academia.edu (original) (raw)

Papers by Fokrul Alom Mazarbhuiya

Research paper thumbnail of Theory and Practice of Mathematics and Computer Science Vol. 2

Research paper thumbnail of Discovering Monthly Fuzzy Patterns

International Journal of Intelligence Science, 2015

Discovering patterns that are fuzzy in nature from temporal datasets is an interesting data minin... more Discovering patterns that are fuzzy in nature from temporal datasets is an interesting data mining problems. One of such patterns is monthly fuzzy pattern where the patterns exist in a certain fuzzy time interval of every month. It involves finding frequent sets and then association rules that holds in certain fuzzy time intervals, viz. beginning of every months or middle of every months, etc. In most of the earlier works, the fuzziness was user-specified. However, in some applications, users may not have enough prior knowledge about the datasets under consideration and may miss some fuzziness associated with the problem. It may be the case that the user is unable to specify the same due to limitation of natural language. In this article, we propose a method of finding patterns that holds in certain fuzzy time intervals of every month where fuzziness is generated by the method itself. The efficacy of the method is demonstrated with experimental results.

Research paper thumbnail of Real-Time Anomaly Detection with Subspace Periodic Clustering Approach

Applied Sciences

Finding real-time anomalies in any network system is recognized as one of the most challenging st... more Finding real-time anomalies in any network system is recognized as one of the most challenging studies in the field of information security. It has so many applications, such as IoT and Stock Markets. In any IoT system, the data generated is real-time and temporal in nature. Due to the extreme exposure to the Internet and interconnectivity of the devices, such systems often face problems such as fraud, anomalies, intrusions, etc. Discovering anomalies in such a domain can be interesting. Clustering and rough set theory have been tried in many cases. Considering the time stamp associated with the data, time-dependent patterns including periodic clusters can be generated, which could be helpful for the efficient detection of anomalies by providing a more in-depth analysis of the system. Another issue related to the aforesaid data is its high dimensionality. In this paper, all the issues related to anomaly detection are addressed, and a clustering-based approach is proposed for finding...

Research paper thumbnail of Detecting IoT Anomaly Using Rough Set and Density Based Subspace Clustering

Research paper thumbnail of An Intuitionistic Fuzzy-Rough Set-Based Classification for Anomaly Detection

Applied Sciences

The challenging issues of computer networks and databases are not only the intrusion detection bu... more The challenging issues of computer networks and databases are not only the intrusion detection but also the reduction of false positives and increase of detection rate. In any intrusion detection system, anomaly detection mainly focuses on modeling the normal behavior of the users and detecting the deviations from normal behavior, which are assumed to be potential intrusions or threats. Several techniques have already been successfully tried for this purpose. However, the normal and suspicious behaviors are hard to predict as there is no precise boundary differentiating one from another. Here, rough set theory and fuzzy set theory come into the picture. In this article, a hybrid approach consisting of rough set theory and intuitionistic fuzzy set theory is proposed for the detection of anomaly. The proposed approach is a classification approach which takes the advantages of both rough set and intuitionistic fuzzy set to deal with inherent uncertainty, vagueness, and indiscernibility...

Research paper thumbnail of A Mixed Clustering Approach for Real Time Anomaly Detection

Anomaly Detection in real time data is accepted as a vital research area. Clustering has effectiv... more Anomaly Detection in real time data is accepted as a vital research area. Clustering has effectively been tried for this purpose. As the datasets are real time, the time of generating of the data is also important. In this article, we introduce a mixture of partitioning and agglomerative hierarchical approach to detect anomalies from such datasets. It is a two-phase method which follows partitioning approach first and then agglomerative hierarchical approach. The dataset can have mixed attributes. In phase-1, a unified metric defined on mixed attributes is used. The same is also used for merging of similar clusters in phase-2. Also, we have kept the track of time attribute of each data instance which produces the clusters with their lifetimes in phase-1. Then in phase-2, we merge the similar clusters. While merging, the similar clusters, the lifetimes of the corresponding clusters with overlapping cores are to be superimposed producing fuzzy time intervals. This way, each cluster wi...

Research paper thumbnail of Clustering of Locally Frequent Patterns over Fuzzy Temporal Datasets

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

Research paper thumbnail of Mining Sequential Patterns from Super Market Datasets

International Journal of Computer Trends and Technology, 2015

Mining sequential patterns is an important data-mining problem and it has many application domain... more Mining sequential patterns is an important data-mining problem and it has many application domains such as Supermarket Medical science, signal processing and speech analysis. The problem involves mining causal relationship between events. Mining sequence from supermarket is an interesting data mining problem. In this paper, we propose a method of mining such patterns. Our approach is completely different from others in the sense that we are interested to find inter-item sets patterns however in other cases patterns are intertransactions. In our case we first find all frequent itemsets where each frequent itemsets is associated with the lists of time intervals in which it is frequent. Sequential patterns can be generated using the lists of time intervals associated with frequent itemsets. The efficacy of the method is established using experimental results

Research paper thumbnail of Mining Local Association Rules from Temporal Data Set

Lecture Notes in Computer Science, 2009

In this paper, we present a novel approach for finding association rules from locally frequent it... more In this paper, we present a novel approach for finding association rules from locally frequent itemsets using rough set and boolean reasoning. The rules mined so are termed as local association rules. The efficacy of the proposed approach is established through experiment over retail dataset that contains retail market basket data from an anonymous Belgian retail store.

Research paper thumbnail of Finding Cyclic Frequent Itemsets

Mining various types of association rules from supermarket datasets is an important data mining p... more Mining various types of association rules from supermarket datasets is an important data mining problem. One similar problem involves finding frequent itemsets and then deriving rules from frequent itemsets. The supermarket data is temporal. Considering time attributes in the supermarket dataset some association rules can be extracted which may hold for a small time interval and not throughout the data gathering period. Such rules are called as local association rules and corresponding frequent itemsets as locally frequent itemsets. Mahanta et al proposes an algorithm for extracting all locally frequent itemsets where each locally frequent itemset is associated with sequence time intervals in which it is frequent. The sequence of time intervals associated with a locally frequent itemsets may exhibit some interesting properties e.g. the itemsets may be cyclic in nature. In this paper we propose an alternative method of finding such cyclic frequent itemsets. The efficacy of the method...

Research paper thumbnail of The State Of The Art Cardiac Illness Prediction Using Novel Data Mining Technique

Data Mining is an analytic process designed to find out data in search of harmonious patterns and... more Data Mining is an analytic process designed to find out data in search of harmonious patterns and methodical relationships between variables, and then to validate the extractive by applying the detected patterns to new subsets of data. The data mining is defined as the procedure of extracting information from enormous sets of data. In other words, we can say that data mining is mining knowledge from data. Afore, the scope of data mining has thoroughly been reviewed and surveyed by many researchers pertaining to the domain of healthcare industry which is an active interdisciplinary area of research. Actually, the task of knowledge extraction from the healthcare industry in medical data is a challenging effort and it is a very complex task. The present scenario in healthcare industry heart illness is a term that assigns to a huge number of health care circumstances related to heart. These medical situations relate to the unexpected health situation that straight control the cardiac. I...

Research paper thumbnail of Copyright © 2011 SciRes. AM Solution of the Fuzzy Equation A + X = B Using the Method of Superimposition

Fuzzy equations were solved by using different standard methods. One of the well-known methods is... more Fuzzy equations were solved by using different standard methods. One of the well-known methods is the method of -cut. The method of superimposition of sets has been used to define arithmetic operations of fuzzy numbers. In this article, it has been shown that the fuzzy equation A X B , where A, X, B are fuzzy numbers can be solved by using the method of superimposition of sets. It has also been shown that the method gives same result as the method of -cut.

Research paper thumbnail of International Journal of Advance Research in Computer Science and Management Studies

Finding patterns from a temporal dataset is a well defined data mining problem. There are many ap... more Finding patterns from a temporal dataset is a well defined data mining problem. There are many approaches to resolve this problem like association rule mining, clustering and classification. Out these clustering has received a lot of attention among the researchers. Clustering is usually used for discovering data distribution and patterns in a dataset. A couple of algorithms have been proposed so far clustering different types of data. Clustering of fuzzy temporal data is an important extension of temporal data clustering. It is actually the method of finding clusters among the frequent itemsets associated with fuzzy time intervals of frequencies. In this paper, we propose an agglomerative hierarchical clustering algorithm to find clusters among the frequent itemsets obtained from fuzzy temporal data. The efficacy of the proposed method is established through experimentation on a synthetic datasets.

Research paper thumbnail of Mining Local Patterns from Fuzzy Temporal Data

International Journal of Engineering and Applied Sciences, 2015

Mining patterns from datasets having fuzzy time attributes is an important data mining problem. S... more Mining patterns from datasets having fuzzy time attributes is an important data mining problem. Some of these mining task are finding locally frequent sets, local association rules etc Most of the earlier works were mainly devoted on mining non-fuzzy temporal datasets .In this article, we propose a method extracting locally frequent itemsets from fuzzy temporal datasets. The efficacy of the method is established with the help of an experiment conducted on a synthetic dataset.

Research paper thumbnail of Comparing Two Methods of Finding Local Association Rules

Mining local association rules from temporal datasets is an interesting data mining problem. Seve... more Mining local association rules from temporal datasets is an interesting data mining problem. Several methods have been developed till today. In this paper, we present a comparative study on traditional rule mining method and that using rough set and boolean reasoning..We propose to show that the method using rough set and boolean reasoning outperfoms the traditional one

Research paper thumbnail of Finding Standard Deviation of a Fuzzy Number

Two probability laws can be root of a possibility law. Considering two probability densities over... more Two probability laws can be root of a possibility law. Considering two probability densities over two disjoint ranges, we can define the fuzzy standard deviation of a fuzzy variable with the help of the standard deviation two random variables in two disjoint spaces.

Research paper thumbnail of From the Symposium Chairs Symposium Program Wednesday, Feb 25 Paper Session 1 Session Chair: Fokrul Alom Mazarbhuiya Session Chair: Neena Thota Professionalism and Quality: What Can Accreditation Offer Engineering?

ABU3QCE 2015 is a local symposium dedicated to the exchange of research and practice focusing on ... more ABU3QCE 2015 is a local symposium dedicated to the exchange of research and practice focusing on enhancing quality in computing education. Contributions cover a broad spectrum of computing education challenges ranging from; computer science, computer engineering, computer information systems, computer information technology to software engineering education. ABU3QCE aims to publish research that combines teaching and learning experience with theoretically founded research within the field. The proceedings papers cover a wide range of topics such as cultural aspects of teaching and learning, technology enhanced teaching, and professional competencies and their role in the curriculum and in higher education.

Research paper thumbnail of Extracting Repitative Patterns from Fuzzy Temporal Data

Association rules mining from temporal dataset is to find associations between items that hold wi... more Association rules mining from temporal dataset is to find associations between items that hold within certain time frame but not throughout the dataset. This problem involves first discovering frequent itemsets which are frequent at certain time intervals and then extracting association rules from such frequent itemsets. In practice, we may have datasets having imprecise or fuzzy time attributes, we term such datasets as fuzzy temporal datasets. In such datasets, as the time of transaction is imprecise, we may have frequent itemsets that are frequent in certain fuzzy time intervals. The algorithm [1] finds all such frequent itemsets along with a collection of list of fuzzy time intervals where each frequent itemset is having an associated list of fuzzy time intervals where it is frequent. The list of fuzzy time intervals may show some interesting features e.g. the itemsets may be repetitive in nature. In this paper we propose a method of finding all repetitive frequent itemsets. The...

Research paper thumbnail of Anomaly Detection Using Agglomerative Hierarchical Clustering Algorithm

Intrusion detection is becoming a hot topic of research for the information security people. Ther... more Intrusion detection is becoming a hot topic of research for the information security people. There are mainly two classes of intrusion detection techniques namely anomaly detection techniques and signature recognition techniques. Anomaly detection techniques are gaining popularity among the researchers and new techniques and algorithms are developing every day. However, no techniques have been found to be absolutely perfect. Clustering is an important data mining techniques used to find patterns and data distribution in the datasets. It is primarily used to identify the dense and sparse regions in the datasets. The sparse regions were often considered as outliers. There are several clustering algorithms developed till today namely K-means, K-medoids, CLARA, CLARANS, DBSCAN, ROCK, BIRCH, CACTUS etc. Clustering techniques have been successfully used for the detection of anomaly in the datasets. The techniques were found to be useful in the design of a couple of anomaly based Intrusion...

Research paper thumbnail of An Efficient Implementation of an Algorithm for Mining Locally Frequent Patterns

Mining patterns from large dataset is an interested data mining problem. Many methods have been d... more Mining patterns from large dataset is an interested data mining problem. Many methods have been developed for this purpose till today. Most of the methods considered the time attributes as one of the normal attribute. However taking the time attribute into account separately the patterns can be extracted which cannot be extracted by normal methods. These patterns are termed as temporal patterns A couple of works have already been done in mining temporal patterns. A nice algorithm for mining locally frequent patterns from temporal datasets is proposed by Anjana et al. In this article, we propose a hash-tree based implementation of the algorithm. We also established the fact that the hash-tree based outperforms others.

Research paper thumbnail of Theory and Practice of Mathematics and Computer Science Vol. 2

Research paper thumbnail of Discovering Monthly Fuzzy Patterns

International Journal of Intelligence Science, 2015

Discovering patterns that are fuzzy in nature from temporal datasets is an interesting data minin... more Discovering patterns that are fuzzy in nature from temporal datasets is an interesting data mining problems. One of such patterns is monthly fuzzy pattern where the patterns exist in a certain fuzzy time interval of every month. It involves finding frequent sets and then association rules that holds in certain fuzzy time intervals, viz. beginning of every months or middle of every months, etc. In most of the earlier works, the fuzziness was user-specified. However, in some applications, users may not have enough prior knowledge about the datasets under consideration and may miss some fuzziness associated with the problem. It may be the case that the user is unable to specify the same due to limitation of natural language. In this article, we propose a method of finding patterns that holds in certain fuzzy time intervals of every month where fuzziness is generated by the method itself. The efficacy of the method is demonstrated with experimental results.

Research paper thumbnail of Real-Time Anomaly Detection with Subspace Periodic Clustering Approach

Applied Sciences

Finding real-time anomalies in any network system is recognized as one of the most challenging st... more Finding real-time anomalies in any network system is recognized as one of the most challenging studies in the field of information security. It has so many applications, such as IoT and Stock Markets. In any IoT system, the data generated is real-time and temporal in nature. Due to the extreme exposure to the Internet and interconnectivity of the devices, such systems often face problems such as fraud, anomalies, intrusions, etc. Discovering anomalies in such a domain can be interesting. Clustering and rough set theory have been tried in many cases. Considering the time stamp associated with the data, time-dependent patterns including periodic clusters can be generated, which could be helpful for the efficient detection of anomalies by providing a more in-depth analysis of the system. Another issue related to the aforesaid data is its high dimensionality. In this paper, all the issues related to anomaly detection are addressed, and a clustering-based approach is proposed for finding...

Research paper thumbnail of Detecting IoT Anomaly Using Rough Set and Density Based Subspace Clustering

Research paper thumbnail of An Intuitionistic Fuzzy-Rough Set-Based Classification for Anomaly Detection

Applied Sciences

The challenging issues of computer networks and databases are not only the intrusion detection bu... more The challenging issues of computer networks and databases are not only the intrusion detection but also the reduction of false positives and increase of detection rate. In any intrusion detection system, anomaly detection mainly focuses on modeling the normal behavior of the users and detecting the deviations from normal behavior, which are assumed to be potential intrusions or threats. Several techniques have already been successfully tried for this purpose. However, the normal and suspicious behaviors are hard to predict as there is no precise boundary differentiating one from another. Here, rough set theory and fuzzy set theory come into the picture. In this article, a hybrid approach consisting of rough set theory and intuitionistic fuzzy set theory is proposed for the detection of anomaly. The proposed approach is a classification approach which takes the advantages of both rough set and intuitionistic fuzzy set to deal with inherent uncertainty, vagueness, and indiscernibility...

Research paper thumbnail of A Mixed Clustering Approach for Real Time Anomaly Detection

Anomaly Detection in real time data is accepted as a vital research area. Clustering has effectiv... more Anomaly Detection in real time data is accepted as a vital research area. Clustering has effectively been tried for this purpose. As the datasets are real time, the time of generating of the data is also important. In this article, we introduce a mixture of partitioning and agglomerative hierarchical approach to detect anomalies from such datasets. It is a two-phase method which follows partitioning approach first and then agglomerative hierarchical approach. The dataset can have mixed attributes. In phase-1, a unified metric defined on mixed attributes is used. The same is also used for merging of similar clusters in phase-2. Also, we have kept the track of time attribute of each data instance which produces the clusters with their lifetimes in phase-1. Then in phase-2, we merge the similar clusters. While merging, the similar clusters, the lifetimes of the corresponding clusters with overlapping cores are to be superimposed producing fuzzy time intervals. This way, each cluster wi...

Research paper thumbnail of Clustering of Locally Frequent Patterns over Fuzzy Temporal Datasets

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

Research paper thumbnail of Mining Sequential Patterns from Super Market Datasets

International Journal of Computer Trends and Technology, 2015

Mining sequential patterns is an important data-mining problem and it has many application domain... more Mining sequential patterns is an important data-mining problem and it has many application domains such as Supermarket Medical science, signal processing and speech analysis. The problem involves mining causal relationship between events. Mining sequence from supermarket is an interesting data mining problem. In this paper, we propose a method of mining such patterns. Our approach is completely different from others in the sense that we are interested to find inter-item sets patterns however in other cases patterns are intertransactions. In our case we first find all frequent itemsets where each frequent itemsets is associated with the lists of time intervals in which it is frequent. Sequential patterns can be generated using the lists of time intervals associated with frequent itemsets. The efficacy of the method is established using experimental results

Research paper thumbnail of Mining Local Association Rules from Temporal Data Set

Lecture Notes in Computer Science, 2009

In this paper, we present a novel approach for finding association rules from locally frequent it... more In this paper, we present a novel approach for finding association rules from locally frequent itemsets using rough set and boolean reasoning. The rules mined so are termed as local association rules. The efficacy of the proposed approach is established through experiment over retail dataset that contains retail market basket data from an anonymous Belgian retail store.

Research paper thumbnail of Finding Cyclic Frequent Itemsets

Mining various types of association rules from supermarket datasets is an important data mining p... more Mining various types of association rules from supermarket datasets is an important data mining problem. One similar problem involves finding frequent itemsets and then deriving rules from frequent itemsets. The supermarket data is temporal. Considering time attributes in the supermarket dataset some association rules can be extracted which may hold for a small time interval and not throughout the data gathering period. Such rules are called as local association rules and corresponding frequent itemsets as locally frequent itemsets. Mahanta et al proposes an algorithm for extracting all locally frequent itemsets where each locally frequent itemset is associated with sequence time intervals in which it is frequent. The sequence of time intervals associated with a locally frequent itemsets may exhibit some interesting properties e.g. the itemsets may be cyclic in nature. In this paper we propose an alternative method of finding such cyclic frequent itemsets. The efficacy of the method...

Research paper thumbnail of The State Of The Art Cardiac Illness Prediction Using Novel Data Mining Technique

Data Mining is an analytic process designed to find out data in search of harmonious patterns and... more Data Mining is an analytic process designed to find out data in search of harmonious patterns and methodical relationships between variables, and then to validate the extractive by applying the detected patterns to new subsets of data. The data mining is defined as the procedure of extracting information from enormous sets of data. In other words, we can say that data mining is mining knowledge from data. Afore, the scope of data mining has thoroughly been reviewed and surveyed by many researchers pertaining to the domain of healthcare industry which is an active interdisciplinary area of research. Actually, the task of knowledge extraction from the healthcare industry in medical data is a challenging effort and it is a very complex task. The present scenario in healthcare industry heart illness is a term that assigns to a huge number of health care circumstances related to heart. These medical situations relate to the unexpected health situation that straight control the cardiac. I...

Research paper thumbnail of Copyright © 2011 SciRes. AM Solution of the Fuzzy Equation A + X = B Using the Method of Superimposition

Fuzzy equations were solved by using different standard methods. One of the well-known methods is... more Fuzzy equations were solved by using different standard methods. One of the well-known methods is the method of -cut. The method of superimposition of sets has been used to define arithmetic operations of fuzzy numbers. In this article, it has been shown that the fuzzy equation A X B , where A, X, B are fuzzy numbers can be solved by using the method of superimposition of sets. It has also been shown that the method gives same result as the method of -cut.

Research paper thumbnail of International Journal of Advance Research in Computer Science and Management Studies

Finding patterns from a temporal dataset is a well defined data mining problem. There are many ap... more Finding patterns from a temporal dataset is a well defined data mining problem. There are many approaches to resolve this problem like association rule mining, clustering and classification. Out these clustering has received a lot of attention among the researchers. Clustering is usually used for discovering data distribution and patterns in a dataset. A couple of algorithms have been proposed so far clustering different types of data. Clustering of fuzzy temporal data is an important extension of temporal data clustering. It is actually the method of finding clusters among the frequent itemsets associated with fuzzy time intervals of frequencies. In this paper, we propose an agglomerative hierarchical clustering algorithm to find clusters among the frequent itemsets obtained from fuzzy temporal data. The efficacy of the proposed method is established through experimentation on a synthetic datasets.

Research paper thumbnail of Mining Local Patterns from Fuzzy Temporal Data

International Journal of Engineering and Applied Sciences, 2015

Mining patterns from datasets having fuzzy time attributes is an important data mining problem. S... more Mining patterns from datasets having fuzzy time attributes is an important data mining problem. Some of these mining task are finding locally frequent sets, local association rules etc Most of the earlier works were mainly devoted on mining non-fuzzy temporal datasets .In this article, we propose a method extracting locally frequent itemsets from fuzzy temporal datasets. The efficacy of the method is established with the help of an experiment conducted on a synthetic dataset.

Research paper thumbnail of Comparing Two Methods of Finding Local Association Rules

Mining local association rules from temporal datasets is an interesting data mining problem. Seve... more Mining local association rules from temporal datasets is an interesting data mining problem. Several methods have been developed till today. In this paper, we present a comparative study on traditional rule mining method and that using rough set and boolean reasoning..We propose to show that the method using rough set and boolean reasoning outperfoms the traditional one

Research paper thumbnail of Finding Standard Deviation of a Fuzzy Number

Two probability laws can be root of a possibility law. Considering two probability densities over... more Two probability laws can be root of a possibility law. Considering two probability densities over two disjoint ranges, we can define the fuzzy standard deviation of a fuzzy variable with the help of the standard deviation two random variables in two disjoint spaces.

Research paper thumbnail of From the Symposium Chairs Symposium Program Wednesday, Feb 25 Paper Session 1 Session Chair: Fokrul Alom Mazarbhuiya Session Chair: Neena Thota Professionalism and Quality: What Can Accreditation Offer Engineering?

ABU3QCE 2015 is a local symposium dedicated to the exchange of research and practice focusing on ... more ABU3QCE 2015 is a local symposium dedicated to the exchange of research and practice focusing on enhancing quality in computing education. Contributions cover a broad spectrum of computing education challenges ranging from; computer science, computer engineering, computer information systems, computer information technology to software engineering education. ABU3QCE aims to publish research that combines teaching and learning experience with theoretically founded research within the field. The proceedings papers cover a wide range of topics such as cultural aspects of teaching and learning, technology enhanced teaching, and professional competencies and their role in the curriculum and in higher education.

Research paper thumbnail of Extracting Repitative Patterns from Fuzzy Temporal Data

Association rules mining from temporal dataset is to find associations between items that hold wi... more Association rules mining from temporal dataset is to find associations between items that hold within certain time frame but not throughout the dataset. This problem involves first discovering frequent itemsets which are frequent at certain time intervals and then extracting association rules from such frequent itemsets. In practice, we may have datasets having imprecise or fuzzy time attributes, we term such datasets as fuzzy temporal datasets. In such datasets, as the time of transaction is imprecise, we may have frequent itemsets that are frequent in certain fuzzy time intervals. The algorithm [1] finds all such frequent itemsets along with a collection of list of fuzzy time intervals where each frequent itemset is having an associated list of fuzzy time intervals where it is frequent. The list of fuzzy time intervals may show some interesting features e.g. the itemsets may be repetitive in nature. In this paper we propose a method of finding all repetitive frequent itemsets. The...

Research paper thumbnail of Anomaly Detection Using Agglomerative Hierarchical Clustering Algorithm

Intrusion detection is becoming a hot topic of research for the information security people. Ther... more Intrusion detection is becoming a hot topic of research for the information security people. There are mainly two classes of intrusion detection techniques namely anomaly detection techniques and signature recognition techniques. Anomaly detection techniques are gaining popularity among the researchers and new techniques and algorithms are developing every day. However, no techniques have been found to be absolutely perfect. Clustering is an important data mining techniques used to find patterns and data distribution in the datasets. It is primarily used to identify the dense and sparse regions in the datasets. The sparse regions were often considered as outliers. There are several clustering algorithms developed till today namely K-means, K-medoids, CLARA, CLARANS, DBSCAN, ROCK, BIRCH, CACTUS etc. Clustering techniques have been successfully used for the detection of anomaly in the datasets. The techniques were found to be useful in the design of a couple of anomaly based Intrusion...

Research paper thumbnail of An Efficient Implementation of an Algorithm for Mining Locally Frequent Patterns

Mining patterns from large dataset is an interested data mining problem. Many methods have been d... more Mining patterns from large dataset is an interested data mining problem. Many methods have been developed for this purpose till today. Most of the methods considered the time attributes as one of the normal attribute. However taking the time attribute into account separately the patterns can be extracted which cannot be extracted by normal methods. These patterns are termed as temporal patterns A couple of works have already been done in mining temporal patterns. A nice algorithm for mining locally frequent patterns from temporal datasets is proposed by Anjana et al. In this article, we propose a hash-tree based implementation of the algorithm. We also established the fact that the hash-tree based outperforms others.