Data Clustering: Algorithms and Applications (Hardcover)

Charu C. Aggarwal, Chandan K. Reddy

  • 出版商: CRC
  • 出版日期: 2013-08-21
  • 售價: $3,650
  • 貴賓價: 9.5$3,468
  • 語言: 英文
  • 頁數: 652
  • 裝訂: Hardcover
  • ISBN: 1466558210
  • ISBN-13: 9781466558212
  • 相關分類: Algorithms-data-structures
  • 立即出貨 (庫存=1)

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商品描述

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.

商品描述(中文翻譯)

研究聚類問題往往在模式識別、數據庫、數據挖掘和機器學習社區之間分散。《數據聚類:算法與應用》以統一的方式解決這個問題,完整涵蓋了聚類的整個領域,從基本方法到更精細和複雜的數據聚類方法。它特別關注圖形、社交網絡和其他領域中的最新問題。

本書重點關注數據聚類的三個主要方面:

1. 方法:描述常用於聚類的關鍵技術,如特徵選擇、凝聚聚類、分區聚類、基於密度的聚類、概率聚類、基於網格的聚類、譜聚類和非負矩陣分解。

2. 領域:涵蓋用於不同數據領域的方法,如分類數據、文本數據、多媒體數據、圖形數據、生物數據、流數據、不確定數據、時間序列聚類、高維聚類和大數據。

3. 變體和見解:討論聚類過程的重要變體,如半監督聚類、交互式聚類、多視圖聚類、聚類集成和聚類驗證。

在本書中,來自世界各地的頂尖研究人員探索了各種應用領域中聚類問題的特點。他們還解釋了如何通過監督、人工干預或自動生成替代聚類來從聚類過程中獲得詳細的洞察力,包括如何驗證底層聚類的質量。