SURVEY ON CLUSTERING ALGORITHM AND SIMILARITY MEASURE FOR CATEGORICAL DATA

Abstract

Learning is the process of generating useful information from a huge volume of data. Learning can be either supervised learning (e.g. classification) or unsupervised learning (e.g. Clustering) Clustering is the process of grouping a set of physical objects into classes of similar object. Objects in real world consist of both numerical and categorical data. Categorical data are not analyzed as numerical data because of the absence of inherit ordering. This paper describes about ten different clustering algorithms, its methodology and the factors influencing its performance. Each algorithm is evaluated using real world datasets and its pro and cons are specified. The various similarity / dissimilarity measure applied to categorical data and its performance is also discussed. The time complexity defines the amount of time taken by an algorithm to perform the elementary operation. The time complexity of various algorithms are discussed and its performance on real world data such as mushroom, zoo, soya bean, cancer, vote, car and iris are measured. In this survey Cluster Accuracy and Error rate for four different clustering algorithm (K-modes, fuzzy K-modes, ROCK and Squeezer), two different similarity measure (DISC and Overlap) and DILCA applied for hierarchy and partition algorithm are evaluated.

Keywords

• Clustering
• Categorical Data
• Time Complexity
• Similarity Measure
• Data Mining Tools