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|Title:||Mining maximal quasi-bicliques to co-cluster stocks and financial ratios for value investment|
|Citation:||Sim, K.,Li, J.,Gopalkrishnan, V.,Liu, G. (2007). Mining maximal quasi-bicliques to co-cluster stocks and financial ratios for value investment. Proceedings - IEEE International Conference on Data Mining, ICDM : 1059-1063. ScholarBank@NUS Repository. https://doi.org/10.1109/ICDM.2006.111|
|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. © 2006 IEEE.|
|Source Title:||Proceedings - IEEE International Conference on Data Mining, ICDM|
|Appears in Collections:||Staff Publications|
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