Clustering categorical data is an integral part of data mining and has attracted much
attention recently. In this paper, we present k-ANMI, a new efficient algorithm for clustering …

Identification of meaningful clusters from categorical data is one key problem in data mining.
Recently, Average Normalized Mutual Information (ANMI) has been used to define …

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 …

Categorical data clustering has attracted much attention recently due to the fact that much of
the data contained in today's databases is categorical in nature. While many algorithms for …

Despite recent efforts, the challenge in clustering categorical and mixed data in the context
of big data still remains due to the lack of inherently meaningful measure of similarity …

Clustering categorical data is an integral part of data mining and has attracted much
attention recently. In this paper, we present k-histogram, a new efficient algorithm for …

Categorical data clustering (CDC) and cluster ensemble (CE) have long been considered as
separate research and application areas. The main focus of this paper is to investigate the …

Clustering is a widely used technique in data mining applications for discovering patterns in
underlying data. Most traditional clustering algorithms are limited to handling datasets that …

The performance of a partitional clustering algorithm is influenced by the initial random
choice of cluster centers. Different runs of the clustering algorithm on the same data set often …

Clustering categorical data poses two challenges defining an inherently meaningful
similarity measure, and effectively dealing with clusters which are often embedded in …