Fuzzy Cluster Analysis
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Fuzzy Cluster Analysis
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Author : Frank Höppner
language : en
Publisher: John Wiley & Sons
Release Date : 1999-07-09
Fuzzy Cluster Analysis written by Frank Höppner and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-07-09 with Science categories.
Dieser Band konzentriert sich auf Konzepte, Algorithmen und Anwendungen des Fuzzy Clustering. In sich geschlossen werden Techniken wie das Fuzzy-c-Mittel und die Gustafson-Kessel- und Gath- und Gava-Algorithmen behandelt, wobei vom Leser keine Vorkenntnisse auf dem Gebiet von Fuzzy-Systemen erwartet werden. Durch anschauliche Anwendungsbeispiele eignet sich das Buch als Einführung für Praktiker der Datenanalyse, der Bilderkennung und der angewandten Mathematik. (05/99)
Advances In Fuzzy Clustering And Its Applications
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Author : Jose Valente de Oliveira
language : en
Publisher: John Wiley & Sons
Release Date : 2007-06-13
Advances In Fuzzy Clustering And Its Applications written by Jose Valente de Oliveira and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-06-13 with Technology & Engineering categories.
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.
Fuzzy C Mean Clustering Using Data Mining
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Author : VIGNESH RAMAMOORTHY H
language : en
Publisher: BookRix
Release Date : 2019-11-28
Fuzzy C Mean Clustering Using Data Mining written by VIGNESH RAMAMOORTHY H and has been published by BookRix this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-28 with Technology & Engineering categories.
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.
Algorithms For Fuzzy Clustering
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Author : Sadaaki Miyamoto
language : en
Publisher: Springer
Release Date : 2009-08-29
Algorithms For Fuzzy Clustering written by Sadaaki Miyamoto and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-08-29 with Computers categories.
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.
Clustering And Fuzzy Techniques
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Author : Hizir
language : en
Publisher: Tenea Verlag Ltd.
Release Date : 2003
Clustering And Fuzzy Techniques written by Hizir and has been published by Tenea Verlag Ltd. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with categories.
Fuzzy Equivalence On Standard And Rough Neutrosophic Sets And Applications To Clustering Analysis
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Author : Nguyen Xuan Thao
language : en
Publisher: Infinite Study
Release Date :
Fuzzy Equivalence On Standard And Rough Neutrosophic Sets And Applications To Clustering Analysis written by Nguyen Xuan Thao and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
In this paper, we propose the concept of fuzzy equivalence on standard neutrosophic sets and rough standard neutrosophic sets. We also provide some formulas for fuzzy equivalence on standard neutrosophic sets and rough standard neutrosophic sets. We also apply these formulas for cluster analysis. Numerical examples are illustrated.
Cluster Analysis For Data Mining And System Identification
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Author : János Abonyi
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-08-10
Cluster Analysis For Data Mining And System Identification written by János Abonyi and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-08-10 with Mathematics categories.
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.
Informational Paradigm Management Of Uncertainty And Theoretical Formalisms In The Clustering Framework A Review
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Author : Pierpaolo D’Urso
language : en
Publisher: Infinite Study
Release Date :
Informational Paradigm Management Of Uncertainty And Theoretical Formalisms In The Clustering Framework A Review written by Pierpaolo D’Urso and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Fifty years have gone by since the publication of the first paper on clustering based on fuzzy sets theory.
Fuzzy Cluster Analysis
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Author : Frank Hoppner
language : en
Publisher:
Release Date : 1999
Fuzzy Cluster Analysis written by Frank Hoppner and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Cluster analysis categories.
Fuzzy Cluster Analysis With Application On Atherosclerosis Disease
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Author : Rand Mohanned Alsheikly
language : en
Publisher: LAP Lambert Academic Publishing
Release Date : 2014-02
Fuzzy Cluster Analysis With Application On Atherosclerosis Disease written by Rand Mohanned Alsheikly and has been published by LAP Lambert Academic Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-02 with categories.
Fuzzy cluster analysis is one of the advanced topics in statistics, which is a tool that assesses the relationships among samples of data set ( many attributes and many observations) by organizing the attributes into different clusters, each observations is belong to any cluster with probability between [0,1]. The fuzzy cluster analysis applied in many fields for example, medicine, cures of diseases or symptoms of diseases, psychiatry, data mining, image processes, pattern recognition, information retrieval.