WEIGHTED TRIPLE-QUALITY (WTQ) ALGORITHM FOR CATEGORICAL DATA

Although attempts have been made to solve the problem of clustering categorical data via cluster ensembles, with the results being competitive to conventional algorithms, it is observed that these techniques unfortunately generate a final data partition based on incomplete information. The underlying ensemble-information matrix presents only cluster-data point relations, with many entries being left unknown. The paper presents an analysis that suggests this problem degrades the quality of the clustering result, and it presents a new link-based approach, which improves the conventional matrix by discovering unknown entries through similarity between clusters in an ensemble. In particular, an efficient link-based algorithm is proposed for the underlying similarity assessment. . A new link-based approach, which improves the conventional matrix by discovering unknown entries through similarity between clusters in an ensemble and an efficient link-based algorithm, is proposed for the underlying similarity assessment. C-Rank link-based algorithm is used to improve clustering quality and ranking clusters in weighted networks.