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 exists an awkward gap between the similarity metrics for categorical and numerical data. This paper therefore presents a general clustering framework based on the concept of objectcluster similarity and gives a unified similarity metric which can be simply applied to the data with categorical, numerical, and mixed attributes. This paper proposes a novel initialization method for mixed data which is implemented using K – Modes algorithm and further and iterative fuzzy K – Modes clustering algorithm. Keyword –Clustering, Similarity Metrics, Plasma compatability, Initialization,k-modes ,fuzzy kmodes,exemplers.