Clustering algorithms on categorical data

Data Clustering is an important part of data mining. Different algorithms are needed to solve the clustering problems which consist of numerical or categorical data because of difference data types. In the literature a lot of algorithms have been found to solve clustering problems with numerical attributes. However, these algorithms are mostly designed to handle numerical data. In this thesis, k mode algorithm and its incremental version are expressed for solving clustering problems in data sets with categorical attributes. It is implemented the algorithms in C and presented the result of the numerical experiments on an example dataset.