  Clustering is widely used in different field such as biology, psychology, and
economics. Most traditional clustering algorithms are limited to handling
datasets that contain either numeric or categorical attributes. However,
datasets with mixed types of attributes are common in real life data mining
applications. In this paper, we review partitioning based algorithm such as
K-prototype, Extension of K-prototype, K-histogram, Fuzzy approaches, genetic
approaches, etc. These algorithm works on both numerical and categorical data.
The approaches has been proposed to handle mixed data are based on four
different perceptive: i) split data set into two part such that each part
contain either numerical or categorical data, then apply separate clustering
algorithm on each data set, finally combined the result of both clustering
algorithm, ii) converting categorical attribute into numerical attribute and
apply numerical attribute clustering algorithm; iii) discrimination of
numerical attribute and apply categorical based clustering algorithm; iv)
Conversion of the categorical attributes into binary ones and apply any
numerical based clustering algorithm
