Clustering And Fuzzy Techniques
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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 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)
Algorithms For Fuzzy Clustering
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Author : Sadaaki Miyamoto
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-04-15
Algorithms For Fuzzy Clustering written by Sadaaki Miyamoto 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 2008-04-15 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.
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.
Fuzzy Clustering Via Proportional Membership Model
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Author : Susana Nascimento
language : en
Publisher: IOS Press
Release Date : 2005
Fuzzy Clustering Via Proportional Membership Model written by Susana Nascimento and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Computers categories.
Development of models with explicit mechanisms for data generation from cluster structures is of major interest in order to provide a theoretical framework for cluster structures found in data. Especially appealing in this regard are the so-called typological structures in which observed entities relate in various degrees to one or several prototypes. Such structures are relevant in many areas such as medicine or marketing, where any entity (patient/consumer) may adhere, with different degrees, to one or several prototypes (clinical scenario/consumer behavior), modelling a typological classification. In fuzzy clustering, the fuzzy c-means (FCM) method has become one of the most popular techniques. As a fuzzy analogue of c-means crisp clustering, FCM models a typological classification, much the same way as c-means. However, FCM does not adhere to the statistical paradigm at which the data are considered generated by a cluster structure, while crisp c-means does. The present work proposes a framework for typological classification based on a fuzzy clustering model of data generation.
Clustering Method Based On Fuzzy Binary Relation
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Author : N. Kondruk
language : en
Publisher: Infinite Study
Release Date :
Clustering Method Based On Fuzzy Binary Relation written by N. Kondruk 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.
One of the most interesting and promising approaches to the analysis of multivariate phenomena and processes are methods of cluster analysis or automatic classification of objects. Clustering is one of the key areas of data mining. Its objective is identification of some unknown structure of a group of similar objects in the initial set.
Innovations In Fuzzy Clustering
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Author : Mika Sato-Ilic
language : en
Publisher: Springer
Release Date : 2006-10-31
Innovations In Fuzzy Clustering written by Mika Sato-Ilic and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-10-31 with Mathematics categories.
This book presents the most recent advances in fuzzy clustering techniques and their applications. The contents include Introduction to Fuzzy Clustering; Fuzzy Clustering based Principal Component Analysis; Fuzzy Clustering based Regression Analysis; Kernel based Fuzzy Clustering; Evaluation of Fuzzy Clustering; Self-Organized Fuzzy Clustering. This book is directed to the computer scientists, engineers, scientists, professors and students of engineering, science, computer science, business, management, avionics and related disciplines.
Advances In Fuzzy Clustering And Its Applications
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Author : Jose Valente de Oliveira
language : en
Publisher: John Wiley & Sons
Release Date : 2007-06-05
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 2007-06-05 with Computers 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 Model Identification
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Author : Hans Hellendoorn
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Fuzzy Model Identification written by Hans Hellendoorn 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 2012-12-06 with Computers categories.
During the past few years two principally different approaches to the design of fuzzy controllers have emerged: heuristics-based design and model-based design. The main motivation for the heuristics-based design is given by the fact that many industrial processes are still controlled in one of the following two ways: - The process is controlled manually by an experienced operator. - The process is controlled by an automatic control system which needs manual, on-line 'trimming' of its parameters by an experienced operator. In both cases it is enough to translate in terms of a set of fuzzy if-then rules the operator's manual control algorithm or manual on-line 'trimming' strategy in order to obtain an equally good, or even better, wholly automatic fuzzy control system. This implies that the design of a fuzzy controller can only be done after a manual control algorithm or trimming strategy exists. It is admitted in the literature on fuzzy control that the heuristics-based approach to the design of fuzzy controllers is very difficult to apply to multiple-inputjmultiple-output control problems which represent the largest part of challenging industrial process control applications. Furthermore, the heuristics-based design lacks systematic and formally verifiable tuning tech niques. Also, studies of the stability, performance, and robustness of a closed loop system incorporating a heuristics-based fuzzy controller can only be done via extensive simulations.
Fuzzy Clustering Models And Applications
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Author : Mika Sato
language : en
Publisher:
Release Date : 1997
Fuzzy Clustering Models And Applications written by Mika Sato and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with categories.