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Pattern Recognition with Fuzzy Objective Function Algorithms
31 Jul 1981-
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Abstract: New updated! The latest book from a very famous author finally comes out. Book of pattern recognition with fuzzy objective function algorithms, as an amazing reference becomes what you need to get. What's for is this book? Are you still thinking for what the book is? Well, this is what you probably will get. You should have made proper choices for your better life. Book, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with.
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08 Sep 2000TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Abstract: The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data
23,600 citations
TL;DR: An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.
Abstract: Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.
14,054 citations
Cites background or methods from "Pattern Recognition with Fuzzy Obje..."
...A generalization of the FCM algorithm was proposed by Bezdek [1981] through a family of...
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...The book by Bezdek [1981] is a good source for material on fuzzy clustering....
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TL;DR: A thorough exposition of community structure, or clustering, is attempted, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists.
Abstract: The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.
9,057 citations
Cites methods from "Pattern Recognition with Fuzzy Obje..."
...Another popular technique, similar in spirit to k-means clustering, is fuzzy k-means clustering (Bezdek, 1981; Dunn, 1974)....
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...Another popular technique, similar in spirit to k-means clustering, is fuzzy k-means clustering (Bezdek, 1981; Dunn, 1973)....
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...In the second step, the vertex points are associated to nc clusters by using fuzzy k-means clustering (Bezdek, 1981; Dunn, 1973) (Section IV....
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TL;DR: A thorough exposition of the main elements of the clustering problem can be found in this paper, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.
8,432 citations
01 Jun 2010
TL;DR: A brief overview of clustering is provided, well known clustering methods are summarized, the major challenges and key issues in designing clustering algorithms are discussed, and some of the emerging and useful research directions are pointed out.
Abstract: Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into a system of ranked taxa: domain, kingdom, phylum, class, etc. Cluster analysis is the formal study of methods and algorithms for grouping, or clustering, objects according to measured or perceived intrinsic characteristics or similarity. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes data clustering (unsupervised learning) from classification or discriminant analysis (supervised learning). The aim of clustering is to find structure in data and is therefore exploratory in nature. Clustering has a long and rich history in a variety of scientific fields. One of the most popular and simple clustering algorithms, K-means, was first published in 1955. In spite of the fact that K-means was proposed over 50 years ago and thousands of clustering algorithms have been published since then, K-means is still widely used. This speaks to the difficulty in designing a general purpose clustering algorithm and the ill-posed problem of clustering. We provide a brief overview of clustering, summarize well known clustering methods, discuss the major challenges and key issues in designing clustering algorithms, and point out some of the emerging and useful research directions, including semi-supervised clustering, ensemble clustering, simultaneous feature selection during data clustering, and large scale data clustering.
6,601 citations
References
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01 Aug 1969
TL;DR: A survey of computer algorithms and philosophies applied to problems of feature extraction and pattern recognition in conjunction with image analysis is presented and the main emphasis is on usable techniques applicable to practical image processing systems.
Abstract: A survey of computer algorithms and philosophies applied to problems of feature extraction and pattern recognition in conjunction with image analysis is presented. The main emphasis is on usable techniques applicable to practical image processing systems. The various methods are discussed under the broad headings of microanalysis and macroanalysis.
153 citations
01 Jan 1977-International Journal of Human-computer Studies \/ International Journal of Man-machine Studies
TL;DR: The main part of the paper consists of a bibliography of some 1150 items, each keyword-indexed with some 750 being classified as concerned with fuzzy system theory and its applications.
Abstract: The main part of the paper consists of a bibliography of some 1150 items, each keyword-indexed with some 750 being classified as concerned with fuzzy system theory and its applications. The remaining items are concerned with closely related topics in many-valued logic, linguistics, the philosophy of vagueness, etc. These background references are annotated in an initial section that outlines the relationship of fuzzy system theory to other developments and provides pointers to various possible fruitful interrelationships. Topics covered include: the philosophy and logic of imprecision and vagueness; other non-standard logics; foundations of set theory; probability theory; fuzzification of mathematical systems; linguistics and psychology; and applications.
152 citations
"Pattern Recognition with Fuzzy Obje..." refers background in this paper
...Many algebraically oriented papers with the flavor of lattice theory and multivalued logic have concerned themselves with extensions of Zadeh's original structure: among the most extensive are those of Goguen(47) and Klaua(66)-see (45) for a more complete discussion and literature survey....
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TL;DR: This volume presents a spectrum of original research works ranging from the very basic properties and characteristics of fuzzy sets to specific areas of applications in the fields of policy analysis and information systems.
Abstract: In this volume, we present a spectrum of original research works ranging from the very basic properties and characteristics of fuzzy sets to specific areas of applications in the fields of policy analysis and information systems. The first part, theory, presents some fine added contributions toward a deeper basic understanding of fuzzy set theory and serves to enrich set theory in the direction of maturity and completeness of its theoretical development.
150 citations
"Pattern Recognition with Fuzzy Obje..." refers background in this paper
...space gUl ance systems, an samp 109 rate control.(53) Papers dealing with related topics-cluster validity for example-will be referenced in more appropriate places....
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149 citations
"Pattern Recognition with Fuzzy Obje..." refers background in this paper
...While not specifically applicable to the cluster validity problem, it seems appropriate to mention here the works of Knopfmacher, (67) who discusses several scalar measures of uncertainty; Halpern,IS6J who attaches to fuzzy graphs a measure of fuzziness having a strong connectivity flavor; and Trillas and Riera, (lOS) who have proposed a number of "algebraic entropies" in connection with the fuzzy integral of SugenoYOl\...
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01 Feb 1977
TL;DR: The fuzzy ISODATA algorithms are used to address two problems: first, the question of feature selection for binary valued data sets is investigated; and second, the same method is applied to the design of a fuzzy one-nearest prototype classifier.
Abstract: The fuzzy ISODATA algorithms are used to address two problems: first, the question of feature selection for binary valued data sets is investigated; and second, the same method is applied to the design of a fuzzy one-nearest prototype classifier The efficiency of this fuzzy classifier is compared to conventional k-NN classifiers by a computational example using the stomach disease data of Scheinok and Rupe, and Toussaint's method for estimation of the probability of misclassification: the fuzzy prototype classifier appears to decrease the error rate expected from all k-NN classifiers by roughly ten per cent
138 citations
"Pattern Recognition with Fuzzy Obje..." refers result in this paper
...Reference (15) exemplifies the use of (13....
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...Bezdek and Castelaz(15) contrasted this to the empirical error rate predicted by (25....
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