An approach to cluster data without distance functions
Shuanghu Luo · Journal of Anhui University · 2001
Data mining has been recognized as a new area for artificial intelligence and database research, and found its profitable applications in many areas. Clustering or segmentation of data is an important data mining application. One of the problems with traditional clustering methods is that they require the analyst to define distance functions that are not always available. In this paper, we propose a new method for clustering without distance functions.