Fuzzy C Mean Clustering Using Data Mining

Author : VIGNESH RAMAMOORTHY H
Publisher : BookRix
Page : 95 pages
File Size : 44,5 Mb
Release : 2019-11-28
Category : Technology & Engineering
ISBN : 9783748722182

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Fuzzy C-mean Clustering using Data Mining by VIGNESH RAMAMOORTHY H Pdf

The goal of traditional clustering is to assign each data point to one and only one cluster. In contrast, fuzzy clustering assigns different degrees of membership to each point. The membership of a point is thus shared among various clusters. This creates the concept of fuzzy boundaries which differs from the traditional concept of well-defined boundaries. In hard clustering, data is divided into distinct clusters, where each data element belongs to exactly one cluster. In fuzzy clustering (also referred to as soft clustering), data elements can belong to more than one cluster, and associated with each element is a set of membership levels. These indicate the strength of the association between that data element and a particular cluster. Fuzzy clustering is a process of assigning these membership levels, and then using them to assign data elements to one or more clusters. This algorithm uses the FCM traditional algorithm to locate the centers of clusters for a bulk of data points. The potential of all data points is being calculated with respect to specified centers. The availability of dividing the data set into large number of clusters will slow the processing time and needs more memory size for the program. Hence traditional clustering should device the data to four clusters and each data point should be located in one specified cluster .Imprecision in data and information gathered from and about our environment is either statistical(e.g., the outcome of a coin toss is a matter of chance) or no statistical (e.g., “apply the brakes pretty soon”). Many algorithms can be implemented to develop clustering of data sets. Fuzzy C-mean clustering (FCM) is efficient and common algorithm. We are tuning this algorithm to get a solution for the rest of data point which omitted because of its farness from all clusters. To develop a high performance algorithm that sort and group data set in variable number of clusters to use this data in control and managing of those clusters.

Author : Sadaaki Miyamoto,Hidetomo Ichihashi,Katsuhiro Honda
Publisher : Springer Science & Business Media
Page : 252 pages
File Size : 46,6 Mb
Release : 2008-04-15
Category : Computers
ISBN : 9783540787365

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Algorithms for Fuzzy Clustering by Sadaaki Miyamoto,Hidetomo Ichihashi,Katsuhiro Honda Pdf

Recently many researchers are working on cluster analysis as a main tool for exploratory data analysis and data mining. A notable feature is that specialists in di?erent ?elds of sciences are considering the tool of data clustering to be useful. A major reason is that clustering algorithms and software are ?exible in thesensethatdi?erentmathematicalframeworksareemployedinthealgorithms and a user can select a suitable method according to his application. Moreover clusteringalgorithmshavedi?erentoutputsrangingfromtheolddendrogramsof agglomerativeclustering to more recent self-organizingmaps. Thus, a researcher or user can choose an appropriate output suited to his purpose,which is another ?exibility of the methods of clustering. An old and still most popular method is the K-means which use K cluster centers. A group of data is gathered around a cluster center and thus forms a cluster. The main subject of this book is the fuzzy c-means proposed by Dunn and Bezdek and their variations including recent studies. A main reasonwhy we concentrate on fuzzy c-means is that most methodology and application studies infuzzy clusteringusefuzzy c-means,andfuzzy c-meansshouldbe consideredto beamajortechniqueofclusteringingeneral,regardlesswhetheroneisinterested in fuzzy methods or not. Moreover recent advances in clustering techniques are rapid and we requirea new textbook that includes recent algorithms.We should also note that several books have recently been published but the contents do not include some methods studied herein.

Author : Junjie Wu
Publisher : Springer Science & Business Media
Page : 187 pages
File Size : 51,7 Mb
Release : 2012-07-09
Category : Computers
ISBN : 9783642298073

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Advances in K-means Clustering by Junjie Wu Pdf

Nearly everyone knows K-means algorithm in the fields of data mining and business intelligence. But the ever-emerging data with extremely complicated characteristics bring new challenges to this "old" algorithm. This book addresses these challenges and makes novel contributions in establishing theoretical frameworks for K-means distances and K-means based consensus clustering, identifying the "dangerous" uniform effect and zero-value dilemma of K-means, adapting right measures for cluster validity, and integrating K-means with SVMs for rare class analysis. This book not only enriches the clustering and optimization theories, but also provides good guidance for the practical use of K-means, especially for important tasks such as network intrusion detection and credit fraud prediction. The thesis on which this book is based has won the "2010 National Excellent Doctoral Dissertation Award", the highest honor for not more than 100 PhD theses per year in China.

Author : János Abonyi,Balázs Feil
Publisher : Springer Science & Business Media
Page : 317 pages
File Size : 52,7 Mb
Release : 2007-06-22
Category : Mathematics
ISBN : 9783764379872

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Cluster Analysis for Data Mining and System Identification by János Abonyi,Balázs Feil Pdf

The aim of this book is to illustrate that advanced fuzzy clustering algorithms can be used not only for partitioning of the data. It can also be used for visualization, regression, classification and time-series analysis, hence fuzzy cluster analysis is a good approach to solve complex data mining and system identification problems. This book is oriented to undergraduate and postgraduate and is well suited for teaching purposes.

Author : Jose Valente de Oliveira,Witold Pedrycz
Publisher : John Wiley & Sons
Page : 454 pages
File Size : 47,6 Mb
Release : 2007-06-13
Category : Technology & Engineering
ISBN : 0470061189

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Advances in Fuzzy Clustering and its Applications by Jose Valente de Oliveira,Witold Pedrycz Pdf

A comprehensive, coherent, and in depth presentation of the state of the art in fuzzy clustering. Fuzzy clustering is now a mature and vibrant area of research with highly innovative advanced applications. Encapsulating this through presenting a careful selection of research contributions, this book addresses timely and relevant concepts and methods, whilst identifying major challenges and recent developments in the area. Split into five clear sections, Fundamentals, Visualization, Algorithms and Computational Aspects, Real-Time and Dynamic Clustering, and Applications and Case Studies, the book covers a wealth of novel, original and fully updated material, and in particular offers: a focus on the algorithmic and computational augmentations of fuzzy clustering and its effectiveness in handling high dimensional problems, distributed problem solving and uncertainty management. presentations of the important and relevant phases of cluster design, including the role of information granules, fuzzy sets in the realization of human-centricity facet of data analysis, as well as system modelling demonstrations of how the results facilitate further detailed development of models, and enhance interpretation aspects a carefully organized illustrative series of applications and case studies in which fuzzy clustering plays a pivotal role This book will be of key interest to engineers associated with fuzzy control, bioinformatics, data mining, image processing, and pattern recognition, while computer engineers, students and researchers, in most engineering disciplines, will find this an invaluable resource and research tool.

Author : Yaochu Jin,Lipo Wang
Publisher : Springer Science & Business Media
Page : 336 pages
File Size : 41,8 Mb
Release : 2009-04-15
Category : Computers
ISBN : 9783540899679

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Fuzzy Systems in Bioinformatics and Computational Biology by Yaochu Jin,Lipo Wang Pdf

Biological systems are inherently stochastic and uncertain. Thus, research in bioinformatics, biomedical engineering and computational biology has to deal with a large amount of uncertainties. Fuzzy logic has shown to be a powerful tool in capturing different uncertainties in engineering systems. In recent years, fuzzy logic based modeling and analysis approaches are also becoming popular in analyzing biological data and modeling biological systems. Numerous research and application results have been reported that demonstrated the effectiveness of fuzzy logic in solving a wide range of biological problems found in bioinformatics, biomedical engineering, and computational biology. Contributed by leading experts world-wide, this edited book contains 16 chapters presenting representative research results on the application of fuzzy systems to genome sequence assembly, gene expression analysis, promoter analysis, cis-regulation logic analysis and synthesis, reconstruction of genetic and cellular networks, as well as biomedical problems, such as medical image processing, electrocardiogram data classification and anesthesia monitoring and control. This volume is a valuable reference for researchers, practitioners, as well as graduate students working in the field of bioinformatics, biomedical engineering and computational biology.

Author : Hizir
Publisher : Tenea Verlag Ltd.
Page : 170 pages
File Size : 44,6 Mb
Release : 2003
Category : Electronic
ISBN : 9783865040398

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Clustering and Fuzzy Techniques by Hizir Pdf

Author : Charles Romesburg
Publisher : Lulu.com
Page : 334 pages
File Size : 47,7 Mb
Release : 2004
Category : Science
ISBN : 9781411606173

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Cluster Analysis for Researchers by Charles Romesburg Pdf

Back in print at a good price. To see the many websites referencing this book, in Google enter "cluster analysis" (in quotes) and Romesburg. Headlines of 5-star reviews on Amazon.com: "A very clear 'how to' book on cluster analysis" (C. Fielitz, Bristol, TN); "An excellent introduction to cluster analysis" (T. W. Powell, Shreveport, LA). A recent (2004) review in Journal of Classification (21:279-283) says: "We should be grateful to the author for his insistence in bringing forth important issues, which have not got yet that level of attention they deserve. I wish this journal could devote more efforts in promoting the scientific inquiry and discussions of methodology of clustering in scientific research [as Cluster Analysis for Researchers does]." To see or search inside the book, go to www.google.com, type in the book's title, and click on it when it comes up (or copy and paste in your browser's window the following URL: http://print.google.com/print?isbn=1411606175 ).

Author : Lakhmi C. Jain,Himansu Sekhar Behera,Jyotsna Kumar Mandal,Durga Prasad Mohapatra
Publisher : Springer
Page : 707 pages
File Size : 55,8 Mb
Release : 2014-12-10
Category : Technology & Engineering
ISBN : 9788132222088

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Computational Intelligence in Data Mining - Volume 2 by Lakhmi C. Jain,Himansu Sekhar Behera,Jyotsna Kumar Mandal,Durga Prasad Mohapatra Pdf

The contributed volume aims to explicate and address the difficulties and challenges that of seamless integration of the two core disciplines of computer science, i.e., computational intelligence and data mining. Data Mining aims at the automatic discovery of underlying non-trivial knowledge from datasets by applying intelligent analysis techniques. The interest in this research area has experienced a considerable growth in the last years due to two key factors: (a) knowledge hidden in organizations’ databases can be exploited to improve strategic and managerial decision-making; (b) the large volume of data managed by organizations makes it impossible to carry out a manual analysis. The book addresses different methods and techniques of integration for enhancing the overall goal of data mining. The book helps to disseminate the knowledge about some innovative, active research directions in the field of data mining, machine and computational intelligence, along with some current issues and applications of related topics.

Author : James C. Bezdek
Publisher : Springer Science & Business Media
Page : 267 pages
File Size : 40,5 Mb
Release : 2013-03-13
Category : Mathematics
ISBN : 9781475704501

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Pattern Recognition with Fuzzy Objective Function Algorithms by James C. Bezdek Pdf

The fuzzy set was conceived as a result of an attempt to come to grips with the problem of pattern recognition in the context of imprecisely defined categories. In such cases, the belonging of an object to a class is a matter of degree, as is the question of whether or not a group of objects form a cluster. A pioneering application of the theory of fuzzy sets to cluster analysis was made in 1969 by Ruspini. It was not until 1973, however, when the appearance of the work by Dunn and Bezdek on the Fuzzy ISODATA (or fuzzy c-means) algorithms became a landmark in the theory of cluster analysis, that the relevance of the theory of fuzzy sets to cluster analysis and pattern recognition became clearly established. Since then, the theory of fuzzy clustering has developed rapidly and fruitfully, with the author of the present monograph contributing a major share of what we know today. In their seminal work, Bezdek and Dunn have introduced the basic idea of determining the fuzzy clusters by minimizing an appropriately defined functional, and have derived iterative algorithms for computing the membership functions for the clusters in question. The important issue of convergence of such algorithms has become much better understood as a result of recent work which is described in the monograph.

Author : Sabu M. Thampi,Ajith Abraham,Sankar Kumar Pal,Juan Manuel Corchado Rodriguez
Publisher : Springer Science & Business Media
Page : 466 pages
File Size : 45,8 Mb
Release : 2013-07-30
Category : Technology & Engineering
ISBN : 9783319017785

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Recent Advances in Intelligent Informatics by Sabu M. Thampi,Ajith Abraham,Sankar Kumar Pal,Juan Manuel Corchado Rodriguez Pdf

This book constitutes the thoroughly refereed post-conference proceedings of the Second International Symposium on Intelligent Informatics (ISI 2013) held in Mysore, India during August 23-24, 2013. The 47 revised papers presented were carefully reviewed and selected from 126 initial submissions. The papers are organized in topical sections on pattern recognition, signal and image processing; data mining, clustering and intelligent information systems; multi agent systems; and computer networks and distributed systems. The book is directed to the researchers and scientists engaged in various fields of intelligent informatics.

Author : Michalis Vazirgiannis,Maria Halkidi,Dimitrious Gunopulos
Publisher : Springer Science & Business Media
Page : 231 pages
File Size : 41,6 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781447100317

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Uncertainty Handling and Quality Assessment in Data Mining by Michalis Vazirgiannis,Maria Halkidi,Dimitrious Gunopulos Pdf

The recent explosive growth of our ability to generate and store data has created a need for new, scalable and efficient, tools for data analysis. The main focus of the discipline of knowledge discovery in databases is to address this need. Knowledge discovery in databases is the fusion of many areas that are concerned with different aspects of data handling and data analysis, including databases, machine learning, statistics, and algorithms. Each of these areas addresses a different part of the problem, and places different emphasis on different requirements. For example, database techniques are designed to efficiently handle relatively simple queries on large amounts of data stored in external (disk) storage. Machine learning techniques typically consider smaller data sets, and the emphasis is on the accuracy ofa relatively complicated analysis task such as classification. The analysis of large data sets requires the design of new tools that not only combine and generalize techniques from different areas, but also require the design and development ofaltogether new scalable techniques.

Author : Charu C. Aggarwal,Chandan K. Reddy
Publisher : CRC Press
Page : 654 pages
File Size : 40,6 Mb
Release : 2018-09-03
Category : Business & Economics
ISBN : 9781315360416

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Data Clustering by Charu C. Aggarwal,Chandan K. Reddy Pdf

Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.

Author : Himansu Sekhar Behera,Durga Prasad Mohapatra
Publisher : Springer
Page : 847 pages
File Size : 47,8 Mb
Release : 2017-05-19
Category : Technology & Engineering
ISBN : 9789811038747

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Computational Intelligence in Data Mining by Himansu Sekhar Behera,Durga Prasad Mohapatra Pdf

The book presents high quality papers presented at the International Conference on Computational Intelligence in Data Mining (ICCIDM 2016) organized by School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India during December 10 – 11, 2016. The book disseminates the knowledge about innovative, active research directions in the field of data mining, machine and computational intelligence, along with current issues and applications of related topics. The volume aims to explicate and address the difficulties and challenges that of seamless integration of the two core disciplines of computer science.

Author : Oded Maimon,Lior Rokach
Publisher : Springer Science & Business Media
Page : 1269 pages
File Size : 46,5 Mb
Release : 2010-09-10
Category : Computers
ISBN : 9780387098234

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Data Mining and Knowledge Discovery Handbook by Oded Maimon,Lior Rokach Pdf

This book organizes key concepts, theories, standards, methodologies, trends, challenges and applications of data mining and knowledge discovery in databases. It first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. It also gives in-depth descriptions of data mining applications in various interdisciplinary industries.