A cluster centers initialization method for clustering categorical data

Keywords: The k-modes algorithm Initialization method Initial cluster centers Density Distance a b s t r a c t The leading partitional clustering technique, k-modes, is one of the most computationally efficient clustering methods for categorical data. However, the performance of the k-modes clustering algorithm which converges to numerous local minima strongly depends on initial cluster centers. Currently, most methods of initialization cluster centers are mainly for numerical data. Due to lack of geometry for the categorical data, these methods used in cluster centers initialization for numerical data are not applicable to categorical data. This paper proposes a novel initialization method for categorical data which is implemented to the k-modes algorithm. The method integrates the distance and the density together to select initial cluster centers and overcomes shortcomings of the existing initialization methods for categorical data. Experimental results illustrate the proposed initialization method is effective and can be applied to large data sets for its linear time complexity with respect to the number of data objects. Clustering is a process of grouping a set of objects into clusters so that the objects in the same cluster have high similarity but are very dissimilar with objects in other clusters. Various types of clustering methods have been proposed and developed, see, for instance (Jain & Dubes, 1988). Clustering algorithms in the literature can generally be classified into two types: hierarchical clustering and partitional clustering. Hierarchical clustering algorithms, essentially heuristic procedures, produce a hierarchy of partitions of the set of observations according to an agglomerative strategy or to a divisive one. Partitional clustering algorithms, in general, assume a given number of clusters and, essentially, seek the optimization of an objective function measuring the homogeneity within the clusters and/or the separation between the clusters. is a well known partitional clustering algorithm which is widely used in real world applications such as marketing research and data mining to cluster very large data sets due to their efficiency. extended the k-means algorithm to propose the k-modes algorithm whose extensions have removed the numeric-only limitation of the k-means algorithm and enable the k-means clustering process to be used to efficiently cluster large categorical data sets from real world databases. Since first published, the k-modes algorithm has become a popular technique in solving categorical data clustering problems in different application domains (Andreopoulos, An, & Wang, 2005). The k-means algorithm and the k-modes algorithm use alternating minimization methods to …