Fuzzy Sets in Information Retrieval and Cluster Analysis, S. Miyamoto

Автор: Abonyi JГЎnos, Feil BalГЎzs
Название: Cluster Analysis for Data Mining and System Identification
ISBN: 3764379871 ISBN-13(EAN): 9783764379872
Издательство: Springer
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Цена: 13969.00 р.
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Описание: This book presents new approaches to data mining and system identification. Algorithmsthat can be used for the clustering of data have been overviewed. New techniques andtools are presented for the clustering, classification, regression and visualization ofcomplex datasets. Special attention is given to the analysis of historical process data,tailored algorithms are presented for the data driven modeling of dynamical systems,determining the model order of nonlinear input-output black box models, and thesegmentation of multivariate time-series. The main methods and techniques areillustrated through several simulated and real-world applications from data mining andprocess engineering practice.The books is aimed primarily at practitioners, researches, and professionals in statistics,data mining, business intelligence, and systems engineering, but it is also accessible tograduate and undergraduate students in applied mathematics, computer science, electricaland process engineering. Familiarity with the basics of system identification and fuzzysystems is helpful but not required.

Автор: S. Miyamoto
Название: Fuzzy Sets in Information Retrieval and Cluster Analysis
ISBN: 0792307216 ISBN-13(EAN): 9780792307211
Издательство: Springer
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Цена: 32144.00 р.
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Описание: The present monograph intends to establish a solid link among three fields: fuzzy set theory, information retrieval, and cluster analysis.

Автор: Slawomir Wierzcho?; Mieczyslaw A. K?opotek
Название: Modern Algorithms of Cluster Analysis
ISBN: 3319693077 ISBN-13(EAN): 9783319693071
Издательство: Springer
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Цена: 22359.00 р.
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Описание: This book provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, cluster analysis etc. The book explains feature-based, graph-based and spectral clustering methods and discusses their formal similarities and differences. Understanding the related formal concepts is particularly vital in the epoch of Big Data; due to the volume and characteristics of the data, it is no longer feasible to predominantly rely on merely viewing the data when facing a clustering problem. Usually clustering involves choosing similar objects and grouping them together. To facilitate the choice of similarity measures for complex and big data, various measures of object similarity, based on quantitative (like numerical measurement results) and qualitative features (like text), as well as combinations of the two, are described, as well as graph-based similarity measures for (hyper) linked objects and measures for multilayered graphs. Numerous variants demonstrating how such similarity measures can be exploited when defining clustering cost functions are also presented. In addition, the book provides an overview of approaches to handling large collections of objects in a reasonable time. In particular, it addresses grid-based methods, sampling methods, parallelization via Map-Reduce, usage of tree-structures, random projections and various heuristic approaches, especially those used for community detection.

Автор: B. S. Duran; P. L. Odell
Название: Cluster Analysis
ISBN: 3540069542 ISBN-13(EAN): 9783540069546
Издательство: Springer
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Цена: 15372.00 р.
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Описание: A tremendous amount of work has been done over the last thirty years in cluster analysis, with a significant amount occurring since 1960. The main intent of the monograph is to give the reader a quick account of the prob- lem of cluster analysis and to expose to him the various aspects thereof.

Автор: Lazzerini
Название: Fuzzy Sets & their Application to Clustering & Training
ISBN: 0849305896 ISBN-13(EAN): 9780849305894
Издательство: Taylor&Francis
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Цена: 26796.00 р.
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Описание: Fuzzy logic applications allow uncertain or imprecise data to be clustered and analyzed when traditional methods cannot be used. This volume offers an introduction to fuzzy set theory and then progresses through the algorithms and techniques used to manipulate data using fuzzy sets, including classification, hierarchy, and cluster structure.

Автор: Donald Metzler
Название: A Feature-Centric View of Information Retrieval
ISBN: 3642270174 ISBN-13(EAN): 9783642270178
Издательство: Springer
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Цена: 13275.00 р.
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Описание: In this work, Metzler describes highly effective information retrieval models for both smaller, classical data sets, and larger Web collections. In a shift away from heuristic, hand-tuned ranking functions and complex probabilistic models, he presents feature-based retrieval models. As he shows, combining term dependencies and arbitrary features results in a very robust, powerful retrieval model.