Mining Maximal Quasi-Bicliques to Co-Cluster Stocks and Financial Ratios for Value Investment

Authors:
Kelvin Sim;Jinyan Li;Vivekanand Gopalkrishnan;Guimei Liu
Affiliations:
Institute for Infocomm Research, Singapore;Institute for Infocomm Research, Singapore;Nanyang Technological University, Singapore;National University of Singapore, Singapore
Venue:
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Year:
2006

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Abstract

We introduce an unsupervised process to co-cluster groups of stocks and financial ratios, so that investors can gain more insight on how they are correlated. Our idea for the co-clustering is based on a graph concept called maximal quasi-bicliques, which can tolerate erroneous or/and missing information that are common in the stock and financial ratio data. Compared to previous works, our maximal quasi-bicliques require the errors to be evenly distributed, which enable us to capture more meaningful co-clusters. We develop a new algorithm that can efficiently enumerate maximal quasi-bicliques from an undirected graph. The concept of maximal quasi-bicliques is domain-independent; it can be extended to perform co-clustering on any set of data that are modeled by graphs.