Clustering Method for Mixed Categorical and Numerical Data

computer engineering

Clustering is the process of discovering a set of categories to which objects should be assigned. A cluster is comprised of a number of similar objects collected or grouped together. The current requirements to cluster real world data sets are scalability, ability to handle any kind of data like categorical and numerical. Traditional algorithm can cluster categorical or numerical data but not the both. Various clustering algorithms have been developed to group data into clusters. However, these clustering algorithms work effectively either on pure numeric data or on pure categorical data, most of them perform poorly on mixed categorical and numerical data types in previous k-means algorithm was used but it is not accurate for large datasets. This study includes the discussion about the different clustering algorithm, its advantages and limitations. An efficient method is proposed for clustering both numerical and categorical data.

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