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A categorical data clustering approa...

Liu, Yu.

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A categorical data clustering approach with expectation maximization and K-nearest neighbour.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A categorical data clustering approach with expectation maximization and K-nearest neighbour./
作者:	Liu, Yu.
面頁冊數:	86 p.
附註:	Source: Masters Abstracts International, Volume: 42-03, page: 0968.
Contained By:	Masters Abstracts International42-03.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MQ84575
ISBN:	0612845753

A categorical data clustering approach with expectation maximization and K-nearest neighbour.
Liu, Yu.

A categorical data clustering approach with expectation maximization and K-nearest neighbour. - 86 p.

Source: Masters Abstracts International, Volume: 42-03, page: 0968.

Thesis (M.Sc.)--University of Windsor (Canada), 2003.

In data mining, clustering analysis is an important research area. The goal of clustering is to group the objects in a data set into meaningful subclasses. Many algorithms have been designed for numerical data clustering and categorical data clustering respectively. However, very few people paid attention to the clustering problem of mixed-type data set which includes data objects that are of both numerical and categorical attributes. This thesis proposes an approach to the solution of this problem. The method is called CCEM-KNN which stands for Categorical data Clustering approach with Expectation Maximization and K-Nearest Neighbour. First, we apply a categorical clustering method over the categorical attributes of the whole data objects to get an initial partition. Then, we apply Expectation-Maximization classification algorithm based on this partition over the numerical attributes of each cluster to create a sample data set. Finally, we apply another classification algorithm K-Nearest Neighbour to perform classification which is based on the sample data set we created. In this way, we finally solve the mixed-type clustering problem. Experiment show that CCEM-KNN performs better than previous work and can also handle large data set well.

ISBN: 0612845753Subjects--Topical Terms:

626642
Computer Science.

A categorical data clustering approach with expectation maximization and K-nearest neighbour.
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502 $a Thesis (M.Sc.)--University of Windsor (Canada), 2003.
520 $a In data mining, clustering analysis is an important research area. The goal of clustering is to group the objects in a data set into meaningful subclasses. Many algorithms have been designed for numerical data clustering and categorical data clustering respectively. However, very few people paid attention to the clustering problem of mixed-type data set which includes data objects that are of both numerical and categorical attributes. This thesis proposes an approach to the solution of this problem. The method is called CCEM-KNN which stands for Categorical data Clustering approach with Expectation Maximization and K-Nearest Neighbour. First, we apply a categorical clustering method over the categorical attributes of the whole data objects to get an initial partition. Then, we apply Expectation-Maximization classification algorithm based on this partition over the numerical attributes of each cluster to create a sample data set. Finally, we apply another classification algorithm K-Nearest Neighbour to perform classification which is based on the sample data set we created. In this way, we finally solve the mixed-type clustering problem. Experiment show that CCEM-KNN performs better than previous work and can also handle large data set well.
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