In this paper, we present continuous research on data analysis based on our previous work on the shrinking approach. Shrinking is a novel data preprocessing technique which optimizes the inner structure of data inspired by the Newton's Universal Law of Gravitationin the real world. It can be applied in many data mining fields. 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. In this paper, we applied the Fuzzy concept to improve the performance of the shrinking approach, targeting the better decision making for the movement for individual data points in each iteration. This approach can assist to improve the performance of existing data analysis approaches.
Digital Object Identifier (DOI)
Yong Shi. 2008. FuzzyShrinking: improving shrinking-based data mining algorithms using fuzzy concept for multi-dimensional data. In Proceedings of the 46th Annual Southeast Regional Conference on XX (ACM-SE 46). ACM, New York, NY, USA, 260-263.