National Changhua University of Education Institutional Repository : Item 987654321/11705

English | 正體中文 | 简体中文 | Items with full text/Total items : 6507/11669
Visitors : 30073638 Online Users : 741

RC Version 3.2 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Scope Adv. Search

Login ‧ Upload ‧ Help ‧ About ‧ Administer

NCUEIR > College of Engineering > Department and Graduate Institute of Computer Science and Information Engineering > Periodical Articles > Item 987654321/11705

Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/11705

Title:	A two-step method for clustering mixed categorical and numeric data
Authors:	Shih, Ming-Yi;Jheng, Jar-Wen;Lai, Lien-Fu
Contributors:	資訊工程學系
Keywords:	Data Mining;Clustering;Mixed Attributes;Co-Occurrence
Date:	2010-01
Issue Date:	2012-07-02T02:03:12Z
Publisher:	淡江大學
Abstract:	Various clustering algorithms have been developed to group data into clusters in diverse domains. However, these clustering algorithms work effectively either on pure numeric data or on pure categorical data, most of them perform poorly on mixed categorical and numeric data types. In this paper, a new two-step clustering method is presented to find clusters on this kind of data. In this approach the items in categorical attributes are processed to construct the similarity or relationships among them based on the ideas of co-occurrence; then all categorical attributes can be converted into numeric attributes based on these constructed relationships. Finally, since all categorical data are converted into numeric, the existing clustering algorithms can be applied to the dataset without pain. Nevertheless, the existing clustering algorithms suffer from some disadvantages or weakness, the proposed two-step method integrates hierarchical and partitioning clustering algorithm with adding attributes to cluster objects. This method defines the relationships among items, and improves the weaknesses of applying single clustering algorithm. Experimental evidences show that robust results can be achieved by applying this method to cluster mixed numeric and categorical data.
Relation:	Tamkang Journal of Science and Engineering (TKJSE), 13(1): 11-19
Appears in Collections:	[Department and Graduate Institute of Computer Science and Information Engineering] Periodical Articles

Files in This Item:

File	Size	Format
index.html	0Kb	HTML	1278	View/Open

Loading...