https://scholars.lib.ntu.edu.tw/handle/123456789/316166
標題: | Labeling unclustered categorical data into clusters based on the important attribute values | 作者: | Chen, Hung-Leng Chuang, Kun-Ta MING-SYAN CHEN |
關鍵字: | Categorical clustering; Data labeling; Data mining | 公開日期: | 2005 | 起(迄)頁: | 106-113 | 來源出版物: | Proceedings - IEEE International Conference on Data Mining, ICDM | 摘要: | Sampling has been recognized as an important technique to improve the efficiency of clustering. However, with sampling applied, those points which are not sampled will not have their labels. Although there is a straightforward approach in the numerical domain, the problem of how to allocate those unlabeled data points into proper clusters remains as a challenging issue in the categorical domain. In this paper, a mechanism named MAximal Resemblance Data Labeling (abbreviated as MARDL) is proposed to allocate each unlabeled data point into the corresponding appropriate cluster based on the novel categorical clustering representative, namely, Node Importance Representative(abbreviated as NIR), which represents clusters by the importance of attribute values. MARDL has two advantages: (1) MARDL exhibits high execution efficiency; (2) after each unlabeled data is allocated into the proper cluster, MARDL preserves clustering characteristics, i.e., high intra-cluster similarity and low inter-cluster similarity. MARDL is empirically validated via real and synthetic data sets, and is shown to be not only more efficient than prior methods but also attaining results of better quality. © 2005 IEEE. |
URI: | http://www.scopus.com/inward/record.url?eid=2-s2.0-34548573371&partnerID=MN8TOARS http://scholars.lib.ntu.edu.tw/handle/123456789/316166 |
ISSN: | 15504786 | DOI: | 10.1109/ICDM.2005.85 | SDG/關鍵字: | Data mining; Data structures; Numerical methods; Sampling; Categorical clustering; Clustering characteristics; Data labeling; Execution efficiency; Cluster analysis |
顯示於: | 電機工程學系 |
檔案 | 描述 | 大小 | 格式 | |
---|---|---|---|---|
01565668.pdf | 190.81 kB | Adobe PDF | 檢視/開啟 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。