Mining Contrast Concepts Representing Differences

Abstract

In this paper, we are concerned with a problem of finding contrast concepts. If someone is interested in studying the cultural differences between different places for a common topic with much attention according to local news reports, it is usually necessary for him/her to read all news at those places and compare them one by one. Obviously, the amount of news is huge and it is extremely difficult and time-consuming. In order to help this kind of task, we try to extract contrast concepts which represent differences between two databases by using Formal Concept Analysis (FCA). To reduce the complexity of constructing concept lattices, we compress concept lattices by using Spectral clustering. We design a top-down depth-first search algorithm equipped with branch-and-bond pruning techniques for mining contrast concepts that drastically reduce the computational complexity.