Fuzzy Clustering Research Papers - Academia.edu (original) (raw)

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- Geology, Geochemistry, Geophysics, Volcanology
- by Patrick Guillaume and +1
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- Mechanical Engineering, Civil Engineering, Structural Dynamics, Structural Health Monitoring
- by Faraj Elmouadib
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- Computer Science, Fuzzy Clustering, Clustering, Cluster Analysis

Social media is said to have an impact on the public discourse and communication in the society. It is increasingly being used in the political context. Social networks sites such as Facebook, Twitter and other microblogging services provide an opportunity for public to give opinions about some issues of interest. Twitter is an ideal platform for users to spread not only information in general but also political opinions, whereas Facebook provides the capability for direct dialogs. A lot of studies have shown that a need exists for stakeholders to collect, monitor, analyze, summarize and visualize these social media views. Some authors have tended to categorize these comments as either positive or negative ignoring the neutral category. In this paper, we demonstrate the importance of the neutral category in the clustering of sentiments from the social media. We then demonstrate the use of fuzzy clustering for this kind of task.

- by Ziren Lu
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- Algorithms, Biomedical Engineering, Fuzzy Logic, Time Series
- by María Guijarro
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- Engineering, Image Processing, Electronics, Machine Vision

Career guidance for students, particularly in rural areas is a challenging issue in India. In the present era
of digitalization, there is a need of an automated system that can analyze a student for his/her capabilities,
suggest a career and provide related information. Keeping in mind the requirement, the present paper is an
effort in this direction. In this paper, a fuzzy based conceptual framework has been suggested. It has two
parts; in the first part a students will be analyzed for his/her capabilities and in the second part the
available courses, job aspects related to their capabilities will be suggested. To analyze a student, marks
in various subject in 10+2 standards and vocational interest in different fields have been considered and
fuzzy sets have been formed. On example basis, fuzzy inference rules have been framed for analyzing the
abilities in engineering, medical and hospitality fields only. In second part, concept of composition of
relations has been used to suggest the related courses and jobs.

- by Karoline Faust
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- Pharmacology, Biochemistry, Bioinformatics, Endocrinology

The increasing demand of World Wide Web raises the need of predicting the user's web page request. The most widely used approach to predict the web pages is the pattern discovery process of Web usage mining. This process involves inevitability of many techniques like Markov model, association rules and clustering. Fuzzy theory with different techniques has been introduced for the better results. Our focus is on Markov models. This paper is introducing the vague Rules with Markov models for more accuracy using the vague set theory.

- by Vidyut Dey
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- Applied Mathematics, Clustering Algorithms, Genetic Algorithm, Fuzzy Clustering
- by Bojana Dalbelo
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- Computer Science, Customer Relationship Management, Retail Banking, Fuzzy
- by Claus Lamm
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- Fuzzy Logic, Magnetic Resonance Imaging, Functional MRI, Imagination
- by Costel Sârbu
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- Engineering, Analytical Chemistry, Principal Component Analysis, Fuzzy Clustering
- by Mahadi Bin Bahari and +1
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- Fuzzy Clustering, Feasibility Study
- by Urszula Markowska-Kaczmar and +1
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- Knowledge Representation, Fuzzy Clustering, Unsupervised Learning, Computational
- by Fajar Masya
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- Computer Science, Data Mining, Clustering and Classification Methods, Forecasting

Mercu Buana University Campus D is part of Mercu Buana University which began the operational in 2013. Since 2013 until 2017, Mercu Buana University Campus D still got less than a target about getting the new student.This can be due to various things including the lack of precisely marketing strategy undertaken.Therefore, in this study the authors make an application by implementing the concept of data mining using clustering and forecasting methods to obtain information from existing data registrants. So, the information can be used by decision makers to determine effective and efficient marketing strategies.

The risk perceived by investors is crucial in the decision to invest, in particular when it concerns a foreign country. The risk associated to any (foreign) investment is a multi-faceted element given that it reflects many aspects that are relevant to (foreign) investors, such as the level of transparency, corruption, rule of law, governance, etc. In this paper we consider the level of economic freedom, as provided by the “Heritage Foundation”, for the most recent years, in order to analyse how is this measure of risk related to the inward foreign direct investment performance index, as provided by the UNCTAD. Given the subjectivity of risk an appropriate methodology consists on using fuzzy logic clustering, which is applied in the paper in order to verify how different the MENA region is from the set of EU-member states. The results show that economic freedom and inward FDI are positively associated, in particular in the cluster of countries that present a higher economic freedom. ...

- by José Caetano
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- Economics, Fuzzy Logic, East Asia, Foreign Direct Investment

Overlapping is one of the topics in wireless sensor networks that is considered by researchers in the last decades. An appropriate overlapping management system can prolong network lifetime and decrease network recovery time. This paper proposes an intelligent and knowledge‐based overlapping clustering protocol for wireless sensor networks, called IKOCP. This protocol uses some of the intelligent and knowledge‐based systems to construct a robust overlapping strategy for sensor networks. The overall network is partitioned to several regions by a proposed multicriteria decision‐making controller to monitor both small‐scale and large‐scale areas. Each region is managed by a sink, where the whole network is managed by a base station. The sensor nodes are categorized by various clusters using the low‐energy adaptive clustering hierarchy (LEACH)‐improved protocol in a way that the value of p is defined by a proposed support vector machine–based mechanism. A proposed fuzzy system determines that noncluster heads associate with several clusters in order to manage overlapping conditions over the network. Cluster heads are changed into clusters in a period by a suggested utility function. Since network lifetime should be prolonged and network traffic should be alleviated, a data aggregation mechanism is proposed to transmit only crucial data packets from cluster heads to sinks. Cluster heads apply a weighted criteria matrix to perform an inner‐cluster routing for transmitting data packets to sinks. Simulation results demonstrate that the proposed protocol surpasses the existing methods in terms of the number of alive nodes, network lifetime, average time to recover, dead time of first node, and dead time of last node.

- by Mohammad Samadi
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- Sensors and Sensing, Data Mining, Sensor, Clustering and Classification Methods
- by Paolo Ceravolo
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- Semantic Web, Fuzzy Clustering, Metadata Extraction, Bottom Up
- by Şakir Esnaf
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- Facility Location, Fuzzy Clustering, Intelligent Manufacturing, Clustering

In today’s age the major problem is related to the predicting user’s web page request. In the past few years the markov model is used for this problem. The effective web mining techniques like Clustering, Association rule mining and markov model having many drawbacks. We are proposing an approach for overcome that drawbacks which help us to improving search engine and helping user to find interesting web pages.

Recently, the quadcopter configuration is becoming prevalent for both civilian and military applications, and research is continuing to improve its controllability. However, most the control methodologies depend on accurate system models which are derived from six degrees of freedom nonlinear system dynamics and they generally do not consider realistic outdoor perturbations and uncertainties. As a solution, model-free data-driven system identification and controlling of quadcopter have been refined in this paper. This nonlinear multiple input-multiple output system identification is proliferated using a fuzzy clustering model. The accuracy of the identification is seen to very high. The results of a PID controller used in conjunction with the identified fuzzy model is also presented. The PID controller with the fuzzy identified model is seen to precisely follow the commanded signals.

- by theam foo ng
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- Mathematics, Computer Science, Pattern Recognition, Fuzzy Clustering

Clustering plays an outstanding role in data mining research. Among the various algorithms for clustering, most of the researchers used the Fuzzy C-Means algorithm (FCM) in the areas like computational geometry, data compression and vector quantization, pattern recognition and pattern classification. In this research, a simple and efficient implementation of FCM clustering algorithm is presented. Three types of inputs are given to algorithm. The data points are first distributed manually, the statistical distributions Normal and Uniform are anther two methods by using the Box-Muller formula. The algorithm is analyzed based on their clustering quality. The behavior of the algorithm depends on the number of data points as well as on the number of cluster. The performance of the algorithm is investigated during different execution of the program on the input data points. The execution time for each cluster and total elapsed time to cluster all the data points is also analyzed and the r...

Globalization and technological innovations create investment opportunities for firms worldwide. In fact, while firms pursue foreign direct investment (FDI) opportunities on a global basis, countries compete to attract these flows. Investment decisions by firms depend on complex and distinct factors. In particular, in the case of foreign investment one of these factors relates to the perception that investors have about the level of risk and/or corruption (or transparency) that characterises countries. Recent studies suggest that corruption negatively impacts on FDI and may act as a disincentive to investment. By using information for 97 countries, concerning inward FDI performance and perceived level of corruption, this paper intends to analyse how corruption influences on the FDI. Given that a certain level of perceived corruption can, in fact, be subject to different subjective evaluations by investors, the paper uses a fuzzy logic approach in order to determine conceivable clust...

- by José Caetano
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- Business, Fuzzy Logic, Political Science, Business Economics

The cluster analysis of fuzzy clustering according to the fuzzy c-means algorithm has been described in this paper: the problem about the fuzzy clustering has been discussed and the general formal concept of the problem of the fuzzy clustering analysis has been presented. The formulation of the problem has been specified and the algorithm for solving it has been described.

- by Tem Journal
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- Fuzzy Clustering, Cluster Analysis, C-Means
- by F Wong
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- Graph Theory, Clustering Algorithms, Image segmentation, Fuzzy Clustering
- by Muammer Ozer
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- Marketing, Music Industry, Fuzzy Clustering, OMEGA

Fuzzy c-means (FCMs) is an important and popular unsupervised partitioning algorithm used in several application domains such as pattern recognition, machine learning and data mining. Although the FCM has shown good performance in detecting clusters, the membership values for each individual computed to each of the clusters cannot indicate how well the individuals are classified. In this paper, a new approach to handle the memberships based on the inherent information in each feature is presented. The algorithm produces a membership matrix for each individual, the membership values are between zero and one and measure the similarity of this individual to the center of each cluster according to each feature. These values can change at each iteration of the algorithm and they are different from one feature to another and from one cluster to another in order to increase the performance of the fuzzy c-means clustering algorithm. To obtain a fuzzy partition by class of the input data set, a way to compute the class membership values is also proposed in this work. Experiments with synthetic and real data sets show that the proposed approach produces good quality of clustering.

- by Bruno Almeida Pimentel
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- Fuzzy Clustering, Multivariate
- by Susana Nascimento
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- Fuzzy Clustering

Diabetes is a serious health issue faced by the society. Classification of tissues in a diabetic wound is highly essential to monitor the healing progress of the wound. The standard manual tissue classification methods utilized by the clinicians are prone to inaccuracy and is highly dependant on the knowledge and experience of the expert. Moreover, since the wounds may form at a body part that is difficult to access, assessing of the wound becomes time-consuming and causes distress to the patient. Hence, an automatic diabetic wound classification system is required for accurate assessment of the wound. In this paper, we offer tissue classification system for diabetic ulcers that utilizes Fuzzy C-Means and colour analysis using colour features to cluster the digital images. The clusters are initially labelled by calculating the minimum Euclidean Distance of the cluster center from the reference points. Each cluster was further analyzed by using spatial relation to label the smaller blobs, and tissue relation to label larger blobs. Granulation and Epithelial were further evaluated by computing the shiny regions caused by the reflection of light on wetter regions of the wound. The proposed algorithm will segment wound tissues into granulation, slough, eschar, and epithelial tissues and display the segmented regions along with the percentage area of each type of tissue. This system can be used by the diagnostician to effectively and easily monitor the healing progress of the wound.

- by M S Krishnamoorthy
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- Computer Science, Fuzzy Clustering, World Wide Web, Emerging Technology

The control and operation of electronic systems
relies and depends on the availability of the power supply.
Rechargeable batteries have been more pervasively used as
the energy storage and power source for various electrical and electronic systems and devices, such as communication
systems, electronic devices, renewable power systems, electric vehicles, etc. However, the rechargeable batteries are
subjected to the availability of the external power source when it is drained out. Because of the concern of battery life,
environmental pollution and a possible energy crisis, the
renewable solar energy has received an increasing attention
in recent years. A fuzzy logic control based grid tied
uninterruptible power supply integrating renewable solar
energy can be used for electrical and electronic systems to
produce power generation. This paper presents the design and implementation of fuzzy logic control based grid tied
uninterruptible power supply integrating the renewable solar power energy system. The uninterruptible power supply (UPS) system is characterized by the rechargeable battery that is connected with the Photovoltaic Panel through the DC/DC converter, the utility AC through the AC/DC converter and the load is connected through the DC/AC converter. The whole operation is controlled by the fuzzy logic algorithm. A complete hardware prototype system model of the fuzzy logic control based on the grid tied uninterruptible power supply integrating with the renewable solar energy is designed and implemented. The operation and effectiveness of the proposed system is then demonstrated by the actual and real-time implementation of the fuzzy logic control grid tied operation
uninterruptible power supply integrating renewable solar
energy connected to the rechargeable battery bank and a PIC
microcontroller platform for fuzzy logic control and operation.

Data mining analyzing amount of data to find relations among them and summarized for getting patterns like, graphical models, statistical models. and etc. These patterns are understandable and useful to their users by employing a range of automated extraction of knowledge from hideaways This Research is a step to illustrate the concept of clustering technique in both theoretical and practical aspects. The theoretical side of the research dealt with the concept of data complexity and types, as well as an explanation of the algorithm (Fcm). The practical side applied the application of the nearest neighboring algorithm to determine the extent of convergence between some date varieties from the adoption of Fructose and and Glucose in Dates. The research concluded that the Fcm algorithm are easy and fast to clarify the genotypes of date species. We use Matlab R2013a to implement the practical side.

The ability of Minkowski Functionals to characterize local structure in different biological tissue types has been demonstrated in a variety of medical image processing tasks. We introduce anisotropic Minkowski Functionals (AMFs) as a novel variant that captures the inherent anisotropy of the underlying gray-level structures. To quantify the anisotropy characterized by our approach, we further introduce a method to compute a quantitative measure motivated by a technique utilized in MR diffusion tensor imaging, namely fractional anisotropy. We showcase the applicability of our method in the research context of characterizing the local structure properties of trabecular bone micro-architecture in the proximal femur as visualized on multi-detector CT. To this end, AMFs were computed locally for each pixel of ROIs extracted from the head, neck and trochanter regions. Fractional anisotropy was then used to quantify the local anisotropy of the trabecular structures found in these ROIs and to compare its distribution in different anatomical regions. Our results suggest a significantly greater concentration of anisotropic trabecular structures in the head and neck regions when compared to the trochanter region (p < 10-4). We also evaluated the ability of such AMFs to predict bone strength in the femoral head of proximal femur specimens obtained from 50 donors. Our results suggest that such AMFs, when used in conjunction with multi-regression models, can outperform more conventional features such as BMD in predicting failure load. We conclude that such anisotropic Minkowski Functionals can capture valuable information regarding directional attributes of local structure, which may be useful in a wide scope of biomedical imaging applications.

- by Titas De
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- Bioengineering, Artificial Intelligence, Computer Vision, Image Processing

Recommender systems have grown to be a critical research subject after the emergence of the first paper on collaborative filtering in the Nineties. Despite the fact that educational studies on recommender systems, has extended extensively over the last 10 years, there are deficiencies in the complete literature evaluation and classification of that research. Because of this, we reviewed articles on recommender structures, and then classified those based on sentiment analysis. The articles are categorized into three techniques of recommender system, i.e.; collaborative filtering (CF), content based and context based. We have tried to find out the research papers related to sentimental analysis based recommender system. To classify research done by authors in this field, we have shown different approaches of recommender system based on sentimental analysis with the help of tables. Our studies give statistics, approximately trends in recommender structures research, and gives practitioners and researchers with perception and destiny route on the recommender system using sentimental analysis. We hope that this paper enables all and sundry who is interested in recommender systems research with insight for destiny.

Cloud computing is Internet-based computing that provides shared computer processing resources and data to computers and other devices on demand. Cloud computing is the hottest purpose built architecture created to support computer users. The cloud addresses three main areas of operation SaaS (software-as-a-service),PaaS (platform-as-a-service) and IaaS (infrastructure as a service). The large amount of data can be stored into cloud Data centers with low cost. We Integrate Data Mining and Cloud Computing to provide a quick access to this large volume amount of data on cloud. This paper aimed to study Clustering algorithm which can be applicable in cloud computing. This paper also describes the role of soft clustering in Cloud computing environment.

- by Jaspreet Singh
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- Organic Chemistry, Fuzzy Clustering, Potato, Phase transition
- by Nikhil Pal
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- Applied Mathematics, Fuzzy Clustering, First-Order Logic, Local minima
- by Wei Pang
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- Engineering, Data Mining, Fuzzy Clustering, Knowledge Based Systems

For a long time, one of my dreams was to describe the nature of uncertainty axiomatically, and it looks like I've finally done it in my co∼eventum mechanics! Now it remains for me to explain to everyone the co∼eventum mechanics in the most approachable way. This is what I'm trying to do in this work. The co∼eventum mechanics is another name for the co∼event theory, i.e., for the theory of experience and chance which I axiomatized in 2016 [1, 2]. In my opinion, this name best reflects the co∼event-based idea of the new dual theory of uncertainty, which combines the probability theory as a theory of chance, with its dual half, the believability theory as a theory of experience. In addition, I like this new name indicates a direct connection between the co∼event theory and quantum mechanics, which is intended for the physical explanation and description of the conict between quantum observers and quantum observations [4]. Since my theory of uncertainty satises the Kolmogorov axioms of probability theory, to explain this co∼eventum mechanics I will use a way analogous to the already tested one, which explains the theory of probability as a theory of chance describing the results of a random experiment. The simplest example of a random experiment in probability theory is the " tossing a coin ". Therefore, I decided to use this the simplest random experiment itself, as well as the two its analogies: the " "flipping a coin " and the " spinning a coin " to explain the co∼eventum mechanics, which describes the results of a combined experienced random experiment. I would like to resort to the usual for the probability theory " coin-based " analogy to explain (and first of all for myself) the logic of the co∼eventum mechanics as a logic of experience and chance. Of course, this analogy one may seem strange if not crazy. But I did not come up with a better way of tying the explanations of the logic of the co∼eventum mechanics to the coin-based explanations that are commonly used in probability theory to explain at least for myself the logic of the chance through a simple visual " coin-based " model that clarifies what occurs as a result of a combined experienced random experiment in which the experience of observer faces the chance of observation. I hope this analogy can be useful not only for me in understanding the co∼eventum mechanics.

- by Babak Rezae
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- Engineering, Cognitive Science, Pure Mathematics, Comparative Study
- by Azadeh Mohammadi
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- Bioinformatics, Genetics, Fuzzy set theory, Cell Cycle

Since the term ''Fuzzy differential equations'' (FDEs) emerged in the literature in 1978, prevailing research effort has been dedicated not only to the development of the concepts concerning the topic, but also to its potential applications. This paper presents a chronological survey on fuzzy differential equations of integer and fractional orders. Attention is concentrated on the FDEs in which a definition of fuzzy derivative of a fuzzy number-valued function has been taken into account. The chronological rationale behind considering FDEs under each concept of fuzzy derivative is highlighted. The pros and cons of each approach dealing with FDEs are also discussed. Moreover, some of the proposed FDEs applications and methods for solving them are investigated. Finally, some of the future perspectives and challenges of fuzzy differential equations are discussed based on our personal view point.

- by William Full
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- Engineering, Earth Sciences, Fuzzy Clustering, Cluster Analysis