Abstract
We define the problem of biclustering on heterogeneous data, that is, data of various types (binary, numeric, etc.). This problem has not yet been investigated in the biclustering literature. We propose a new method, HBC (Heterogeneous BiClustering), designed to extract biclusters from heterogeneous, large-scale, sparse data matrices. The goal of this method is to handle medical data gathered by hospitals (on patients, stays, acts, diagnoses, prescriptions, etc.) and to provide valuable insight on such data. HBC takes advantage of the data sparsity and uses a constructive greedy heuristic to build a large number of possibly overlapping biclusters. The proposed method is successfully compared with a standard biclustering algorithm on small-size numeric data. Experiments on real-life data sets further assert its scalability and efficiency.
C. Dhaenens—This work was partially supported by project ClinMine - ANR-13-TECS-0009.
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Vandromme, M., Jacques, J., Taillard, J., Jourdan, L., Dhaenens, C. (2016). A Scalable Biclustering Method for Heterogeneous Medical Data. In: Pardalos, P., Conca, P., Giuffrida, G., Nicosia, G. (eds) Machine Learning, Optimization, and Big Data. MOD 2016. Lecture Notes in Computer Science(), vol 10122. Springer, Cham. https://doi.org/10.1007/978-3-319-51469-7_6
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