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Clustering Methods for Mixed-Type Data.

Foss, Alexander Hawthorne.

Clustering Methods for Mixed-Type Data.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Clustering Methods for Mixed-Type Data./
作者:	Foss, Alexander Hawthorne.
面頁冊數:	1 online resource (260 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:	Dissertation Abstracts International78-11B(E).
標題:	Biostatistics. -
電子資源:	click for full text (PQDT)
ISBN:	9780355046106

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

Clustering Methods for Mixed-Type Data. - 1 online resource (260 pages)

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

Thesis (Ph.D.)

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355046106Subjects--Topical Terms:

783654
Biostatistics.
Index Terms--Genre/Form:

554714
Electronic books.

Clustering Methods for Mixed-Type Data.
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502 $a Thesis (Ph.D.) $c State University of New York at Buffalo $d 2017.
504 $a Includes bibliographical references
520 $a 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.
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538 $a Mode of access: World Wide Web
650 4 $a Biostatistics. $3 783654
650 4 $a Statistics. $3 556824
650 4 $a Computer science. $3 573171
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690 $a 0463
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