An Interactive Tool for Constrained Clustering with Human Sampling
Masayuki Okabe, Seiji Yamada · 2010
This paper describes an interactive tool for constrained clustering that helps users to select effective constraints efficiently during the constrained clustering process. This tool has some functions such as 2-D visual arrangement of a data set and constraint assignment by mouse manipulation. Moreover, it can execute distance metric learning and k-medoids clustering. In this paper, we show the overview of the tool and how it works, especially in the functions of display arrangement by multi-dimensional scaling and incremental distance metric learning. Eventually we show a preliminary experiment in which human heuristics found through our GUI improve the clustering. This study provides fundamental technologies for interactive clustering of Web page and Web usages.