On Data Labeling for Clustering Categorical Data

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 after the normal process. 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, N-Nodeset Importance Representative (abbreviated as NNIR), which represents clusters by the importance of the combinations of attribute values. MARDL has two advantages: 1) MARDL exhibits high execution efficiency and 2) MARDL can achieve high intracluster similarity and low intercluster similarity, which are regarded as the most important properties of clusters, thus benefiting the analysis of cluster behaviors. MARDL is empirically validated on real and synthetic data sets, and is shown to be significantly more efficient than prior works while attaining results of high quality.