  Clustering is a widely used technique in data mining applications for
discovering patterns in underlying data. 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 propose a novel
divide-and-conquer technique to solve this problem. First, the original mixed
dataset is divided into two sub-datasets: the pure categorical dataset and the
pure numeric dataset. Next, existing well established clustering algorithms
designed for different types of datasets are employed to produce corresponding
clusters. Last, the clustering results on the categorical and numeric dataset
are combined as a categorical dataset, on which the categorical data clustering
algorithm is used to get the final clusters. Our contribution in this paper is
to provide an algorithm framework for the mixed attributes clustering problem,
in which existing clustering algorithms can be easily integrated, the
capabilities of different kinds of clustering algorithms and characteristics of
different types of datasets could be fully exploited. Comparisons with other
clustering algorithms on real life datasets illustrate the superiority of our
approach.
