National Changhua University of Education Institutional Repository : Item 987654321/11705
English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 6507/11669
造訪人次 : 30109499      線上人數 : 592
RC Version 3.2 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜尋範圍 進階搜尋

請使用永久網址來引用或連結此文件: http://ir.ncue.edu.tw/ir/handle/987654321/11705

題名: A two-step method for clustering mixed categorical and numeric data
作者: Shih, Ming-Yi;Jheng, Jar-Wen;Lai, Lien-Fu
貢獻者: 資訊工程學系
關鍵詞: Data Mining;Clustering;Mixed Attributes;Co-Occurrence
日期: 2010-01
上傳時間: 2012-07-02T02:03:12Z
出版者: 淡江大學
摘要: 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.
關聯: Tamkang Journal of Science and Engineering (TKJSE), 13(1): 11-19
顯示於類別:[資訊工程學系] 期刊論文

文件中的檔案:

檔案 大小格式瀏覽次數
index.html0KbHTML1278檢視/開啟


在NCUEIR中所有的資料項目都受到原著作權保護.

 

DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回饋