Labeling Unclustered Categorical Data into Clusters Based on the Important Attribute Values

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 inte...

• Type: Conference
• Year: 2005
• Where: ICDM
• Authors: Hung-Leng Chen, Kun-Ta Chuang, Ming-Syan Chen
• Keywords: Data Mining, ICDM 2005, Proper Cluster, Unlabeled Data, Unlabeled Data Point