Multidimensional data has been a challenge for data analysis because of the inherent sparsely of the points. In this
paper, we have present a novel data preprocessing technique called shrinking which optimizes the inherent characteristic of
distribution of data. This data reorganization concept can be applied in many fields such as pattern recognition, data clustering
and signal processing. Then, as an important application of the data shrinking preprocessing, we propose a shrinking-based
approach for multi-dimensional data analysis which consists of three steps: data shrinking, cluster detection, and cluster
evaluation and selection. The process of data shrinking moves data points along the direction of the density gradient, thus
generating condensed, widely-separated clusters. The data-shrinking and cluster-detection steps are conducted on a sequence
of grids with different cell sizes. The clusters detected at these scales are compared by a cluster-wise evaluation measurement,
and the best clusters are selected as the final result. This paper shows that this approach can effectively and efficiently detect
clusters in both low- and high-dimensional spaces.