Numerical and Categorical Attributes Data Clustering Using K-Modes and Fuzzy K-Modes

Most of the existing clustering approaches are applicable to purely numerical or categorical data only, but not the both. In general, it is a nontrivial task to perform clustering on mixed data composed of numerical and categorical attributes because there exist an awkward gap between the similarity metrics for categorical and numerical data. Therefore, this paper presents a general clustering framework based on the concept of object-cluster similarity and gives a unified similarity metric which can be simply applied to the data with categorical, numerical, and mixed attributes.

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International Journal of Engineering Research and Applications (IJERA)
Big Data