A Review on K-Mode Clustering Algorithm

Abstract

The main purpose of the process of data mining is to extract useful information from a huge amount of dataset. As one of the most important tasks in data mining, clustering is the process of grouping object attributes and features such that the data objects in one group are more similar than data objects in another group. It is a form of unsupervised learning that means how data should be grouped the data objects (similar types) together will be not known in advance. The algorithms used for clustering are k-means algorithm, k-medoid algorithm, k-nearest neighbour algorithm, k-mode algorithm etc. The K-Mode Algorithm is an eminent algorithm which is an extension of the K-Means Algorithm for clustering data set with categorical attributes and is famous for its simplicity and speed. The 'Simple Matching Dissimilarity' measure is used instead of Euclidean distance and the 'Mode' of clusters is used instead of 'Means'. In this paper, review on the K-Mode Algorithm is done.