Clustering mixed type data: a space structure-based approach

Clustering mixed type data is important for the areas such as knowledge discovery and machine learning. Although many clustering algorithms have been developed for mixed type data, clustering mixed type data is still a challenging task. The challenges mainly come from the fact that the numerical attributes and categorical attributes of mixed type data are not in the same space. Most of the mixed data clustering methods handle the two types of attributes separately. The gap between the numerical attributes and categorical attributes is not handled very well. To handle the above issues, we expand the space structure representation scheme for categorical data to mixed type data. In the new scheme, all the attributes of the mixed type data are expressed as the numerical type, which is in a Euclidean space. In addition, we propose an accelerated approximate space structure based on the Nyström method, which reduces the time cost and memory cost of constructing a space structure. We then propose general frameworks based on the space structure data (SBM) and accelerated approximate space structure (Ap-SBM) for mixed type data clustering. Experimental analyses reflect the ability of the space structure to express the original mixed type data and the ability of the accelerated approximate space structure to express the space structure. The experimental results on thirteen mixed type data sets from UCI show superiority of the proposed frameworks compared with the other six representative mixed type data clustering algorithms.