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The K-Means Tree.

Gao, Kun.

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The K-Means Tree.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	The K-Means Tree./
作者:	Gao, Kun.
面頁冊數:	73 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-03, Section: B, page: 1320.
Contained By:	Dissertation Abstracts International64-03B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3084293

The K-Means Tree.
Gao, Kun.

The K-Means Tree. - 73 p.

Source: Dissertation Abstracts International, Volume: 64-03, Section: B, page: 1320.

Thesis (Ph.D.)--Yale University, 2003.

Due to the fast development and wide use of computer technologies in recent years, data sets have become increasingly large and complicated. There has been growing emphasis on exploratory analysis of very large datasets to discover clusters both to reduce the size of later calculations, and also to identify interesting subsets for further exploration. However, most existing clustering methods become slow and inefficient as the size of dataset increases.Subjects--Topical Terms:

626642
Computer Science.

The K-Means Tree.
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500 $a Source: Dissertation Abstracts International, Volume: 64-03, Section: B, page: 1320.
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502 $a Thesis (Ph.D.)--Yale University, 2003.
520 $a Due to the fast development and wide use of computer technologies in recent years, data sets have become increasingly large and complicated. There has been growing emphasis on exploratory analysis of very large datasets to discover clusters both to reduce the size of later calculations, and also to identify interesting subsets for further exploration. However, most existing clustering methods become slow and inefficient as the size of dataset increases.
520 $a In this thesis, we propose a new fast clustering algorithm called the <italic> K</italic>-Means Tree. This method builds a tree iteratively by first passing data points into the previously constructed tree one at a time, and then refining the tree by further re-allocating the data points to the tree one-at-a-time. We compare the speed and cluster quality of the <italic>K</italic>-Means Tree with existing algorithms, and show that it has comparable speed to the fastest competing algorithm, and produces clusters of comparable quality to the <italic> K</italic>-Means clusters.
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