A New Clustering Algorithm in Categorical Data Approach

Data clustering is a basic tool for understanding the structure of data collections. The process puts the data into groups of similar objects is called clustering. Clustering is one of the main issues of unsupervised clustering to find the structure in a set of unlabeled data. Clustering algorithms can be divided into two categories according to the type of data: Clustering algorithms for numerical data and clustering algorithms for categorical data. The clustering algorithms for categorical data are more important than clustering algorithms for numerical data because of the nature and application of these data. In this paper, at first the nature of this type of data is described and then the clustering algorithms and similarity measures presented in this area are reviewed. Finally, a hybrid method is proposed based on the combination of the hierarchical clustering algorithm and the partitioning clustering algorithm. The experiments show that the proposed method in this paper improves the results of clustering.