Discovering Substantial Distinctions among Incremental Bi-Clusters
Faris Alqadah, Raj Bhatnagar · 2009
A fundamental task of data analysis is comprehending what distinguishes clusters found within the data. We present the problem of mining distinguishing sets which seeks to find sets of objects or attributes that induce that most change among the incremental bi-clusters of a binary dataset. Unlike emerging patterns and contrast sets which only focus on statistical differences between support of itemsets, our approach considers distinctions in both the attribute space and the object space. Viewing the lattice of bi-clusters formed within a data set as a weighted directed graph, we mine the most significant distinguishing sets by growing a maximal cost spanning tree of the lattice. In this paper we present a weighting function for measuring distinction among bi-clusters in the lattice and the novel MIDS algorithm. MIDS simultaneously enumerates bi-clusters, constructs the bi-cluster lattice, and computes the distinguishing sets. The efficient computational performance of MIDS is exhibited in a performance test on real world and benchmark data sets. The utility of distinguishing sets is also demonstrated with experiments on synthetic and real data.