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Clustering Methods for Mixed-Type Data.

Foss, Alexander Hawthorne.

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Clustering Methods for Mixed-Type Data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Clustering Methods for Mixed-Type Data./
Author:	Foss, Alexander Hawthorne.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	260 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:	Dissertation Abstracts International78-11B(E).
Subject:	Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10278781
ISBN:	9780355046106

Clustering Methods for Mixed-Type Data.
Foss, Alexander Hawthorne.

Clustering Methods for Mixed-Type Data. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 260 p.

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.

Thesis (Ph.D.)--State University of New York at Buffalo, 2017.

As massive data sets become increasingly common, much attention has been paid to issues relating to large sample size. However, massive data sets often involve a large number of variables that are often heterogeneous in nature. In this dissertation I seek to develop novel techniques for clustering mixed-type data consisting of continuous and nominal variables. I first review the literature on clustering mixed-type data and identify the strengths and weaknesses of existing methods. I next propose a clustering technique (KAMILA) that overcomes the central weaknesses in current state-of-the-art methods. This novel method is suitable for very large data sets, and is compatible with a map-reduce computing framework; implementations in both R and Hadoop are discussed. Finally, I discuss the related issues of variable weighting and variable selection in clustering mixed-type data.

ISBN: 9780355046106Subjects--Topical Terms:

1002712
Biostatistics.

Clustering Methods for Mixed-Type Data.
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