登入選單

返回Google圖書搜尋

Introduction to Clustering Large and High-Dimensional Data

Jacob Kogan

出版	Cambridge University Press, 2007
主題	Computers / Database Administration & ManagementComputers / Data Science / Data AnalyticsComputers / System Administration / Storage & RetrievalComputers / Optical Data ProcessingComputers / Programming / Object OrientedMathematics / Set TheoryMathematics / Probability & Statistics / General
ISBN	05216179369780521617932
URL	http://books.google.com.hk/books?id=AdfSSGncSlwC&hl=&source=gbs_api
EBook	SAMPLE

註釋

There is a growing need for a more automated system of partitioning data sets into groups, or clusters. For example, digital libraries and the World Wide Web continue to grow exponentially, the ability to find useful information increasingly depends on the indexing infrastructure or search engine. Clustering techniques can be used to discover natural groups in data sets and to identify abstract structures that might reside there, without having any background knowledge of the characteristics of the data. Clustering has been used in a variety of areas, including computer vision, VLSI design, data mining, bio-informatics (gene expression analysis), and information retrieval, to name just a few. This book focuses on a few of the most important clustering algorithms, providing a detailed account of these major models in an information retrieval context. The beginning chapters introduce the classic algorithms in detail, while the later chapters describe clustering through divergences and show recent research for more advanced audiences.