A scalable biclustering method for heterogeneous medical data
2016
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 biclus-
ters 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 con-
structive greedy heuristic to build a large number of possibly overlapping
biclusters. The proposed method is successfully compared with a stan-
dard biclustering algorithm on small-size numeric data. Experiments on
real-life data sets further assert its scalability and efficiency.
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