Introduction to Clustering Large and High-Dimensional Data

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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.

High-Dimensional Probability: An Introduction with Applications in Data Science (1版)

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書名：High-Dimensional Probability: An Introduction with Applications in Data Science 作者：Vershynin 出版社：CAMBRIDGE 出版日期：2018/09/30 ISBN：9781108415194

High-Dimensional Statistics

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書名：High-Dimensional Statistics: A Non-Asymptotic Viewpoint 作者：Wainwright 出版社：CAMBRIDGE 出版日期：2019/04/00 ISBN：9781108498029