Data Clustering: Algorithms and Applications

Data Clustering: Algorithms and Applications

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Hardcover, Taylor & Francis, 2014, ISBN13 9781466558212, ISBN10 1466558210
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"Clustering is a diverse topic, and the underlying algorithms depend greatly on the data domain and problem scenario. This book focuses on three primary aspects of data clustering: the core methods such as probabilistic, density-based, grid-based, and spectral clustering etc; different problem domains and scenarios such as multimedia, text, biological, categorical, network, and uncertain data as well as data streams; and different detailed insights from the clustering process because of the subjectivity of the clustering process, and the many different ways in which the same data set can be clustered"--

About this item

Important Made in USA Origin Disclaimer: For certain items sold by Walmart on Walmart.com, the displayed country of origin information may not be accurate or consistent with manufacturer information. For updated, accurate country of origin data, it is recommended that you rely on product packaging or manufacturer information.

"Clustering is a diverse topic, and the underlying algorithms depend greatly on the data domain and problem scenario. This book focuses on three primary aspects of data clustering: the core methods such as probabilistic, density-based, grid-based, and spectral clustering etc; different problem domains and scenarios such as multimedia, text, biological, categorical, network, and uncertain data as well as data streams; and different detailed insights from the clustering process because of the subjectivity of the clustering process, and the many different ways in which the same data set can be clustered"--

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.

Specifications

Number of Pages: 622
Original Languages: English
Author: Aggarwal, Charu C. ,  Reddy, Chandan K.
Series Title: Chapman & Hall / CRC Data Mining and Knowledge Discovery
Publisher: Taylor & Francis
ISBN-13: 9781466558212
Assembled Product Dimensions (L x W x H): 7.25 x 10.00 x 1.50 Inches
ISBN-10: 1466558210
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