TY - JFULL AU - S.Aranganayagi and K.Thangavel PY - 2010/2/ TI - Incremental Algorithm to Cluster the Categorical Data with Frequency Based Similarity Measure T2 - International Journal of Computer and Information Engineering SP - 167 EP - 176 VL - 4 SN - 1307-6892 UR - https://publications.waset.org/pdf/14709 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 37, 2010 N2 - Clustering categorical data is more complicated than the numerical clustering because of its special properties. Scalability and memory constraint is the challenging problem in clustering large data set. This paper presents an incremental algorithm to cluster the categorical data. Frequencies of attribute values contribute much in clustering similar categorical objects. In this paper we propose new similarity measures based on the frequencies of attribute values and its cardinalities. The proposed measures and the algorithm are experimented with the data sets from UCI data repository. Results prove that the proposed method generates better clusters than the existing one. ER -