A Dimension Reduction Approach Using Shrinking for Multi-Dimensional Data Analysis

In this paper, we present ongoing research on data analysis based on our previous work on the shrinking approach. Shrinking [22] is a novel data preprocessing technique which optimizes the inner structure of data. It can be applied in many data mining fields. Following our previous work on the shrinking method for multi-dimensional data analysis in full data space, we propose a shrinking-based dimension reduction approach which tends to solve the dimension reduction problem from a new perspective. In this approach data are moved along the direction of the density gradient, thus making the inner structure of data more prominent. It is conducted on a sequence of grids with different cell sizes. Dimension reduction process is performed based on the difference of the data distribution projected on each dimension before and after the data-shrinking process. Those dimensions with dramatic variation of data distribution through the data-shrinking process are selected as good dimension candidates for further data analysis. This approach can assist to improve the performance of existing data analysis approaches. We demonstrate how this shrinking-based dimension reduction approach affects the clustering results of well-known algorithms.