How to measure similarity for multiple categorical data sets?

How to measure similarity or distance for multiple categorical data? It is an important step for Data Mining and Knowledge Management process to measure similarity or distance between objects appropriately. Measurements for continuous data have been well-defined and relatively easy to be calculated....

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Bibliographic Details
Published in:Multimedia tools and applications 2015-05, Vol.74 (10), p.3489-3505
Main Authors: Park, Simon Soon-Hyoung, Song, Justin JongSu, Lee, James Jung-Hoon, Lee, Wookey, Ree, Sangbok
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Classification
Clustering
Computer Communication Networks
Computer Science
Data mining
Data Structures and Information Theory
Datasets
Fuzzy sets
Graphs
Industrial engineering
Knowledge management
Multimedia computer applications
Multimedia Information Systems
Similarity measures
Special Purpose and Application-Based Systems
Studies
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Summary:How to measure similarity or distance for multiple categorical data? It is an important step for Data Mining and Knowledge Management process to measure similarity or distance between objects appropriately. Measurements for continuous data have been well-defined and relatively easy to be calculated. However, the notion of similarity for categorical data is not simple, since categorical data usually is not simply translated into the numerical format, and they also have their own priority with structures and data distribution. In this paper, we propose a new measure for multiple categorical data sets using data distribution. Our new measure, MCSM (Multiple Categorical Similarity Measure), can solve conventional drawbacks of multiple categorical data sets successfully in which we prove the verification of our measure with mathematical proofs and experimentation. The experimental result shows that our measure is powerful for multiple categorical data sets with proper data distributions.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-014-1914-5