DISC: Data-Intensive Similarity Measure for Categorical Data

Abstract. The concept of similarity is fundamentally important in almost every scientific field. Clustering, distance-based outlier detection, classification, regression and search are major data mining techniques which compute the similarities between instances and hence the choice of a particular similarity measure can turn out to be a major cause of success or failure of the algorithm. The notion of similarity or distance for categorical data is not as straightforward as for continuous data and hence, is a major challenge. This is due to the fact that different values taken by a categorical attribute are not inherently ordered and hence a notion of direct comparison between two categorical values is not possible. In addition, the notion of similarity can differ depending on the particular domain, dataset, or task at hand. In this paper we present a new similarity measure for categorical data DISC - Data-Intensive Similarity Measure for Categorical Data. DISC captures the semant...

• Type: Journal
• Year: 2011
• Where: PAKDD
• Authors: Aditya Desai, Himanshu Singh, Vikram Pudi
• Keywords: Data Mining, Data Mining Techniques, PAKDD 2011, Similarity Measure, Similarity Measures