Labeling unclustered categorical data into clusters based on the important attribute values

Hung Leng Chen, Kun Ta Chuang, Ming Syan Chen

研究成果: Conference contribution

13 引文 斯高帕斯(Scopus)

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.

原文English
主出版物標題Proceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005
頁面106-113
頁數8
DOIs
出版狀態Published - 2005 十二月 1
事件5th IEEE International Conference on Data Mining, ICDM 2005 - Houston, TX, United States
持續時間: 2005 十一月 272005 十一月 30

出版系列

名字Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN(列印)1550-4786

Other

Other5th IEEE International Conference on Data Mining, ICDM 2005
國家/地區United States
城市Houston, TX
期間05-11-2705-11-30

All Science Journal Classification (ASJC) codes

  • 工程 (全部)

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