A scalable biclustering method for heterogeneous medical data

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 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.
Type de document :
Communication dans un congrès
International Workshop on Machine Learning, Optimization and Big Data, Aug 2016, Volterra, Italy. 10122, pp.12, Lecture Notes in Computer Science
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https://hal.inria.fr/hal-01420947
Contributeur : Maxence Vandromme <>
Soumis le : mercredi 21 décembre 2016 - 11:56:34
Dernière modification le : vendredi 13 avril 2018 - 01:28:05

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  • HAL Id : hal-01420947, version 1

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Maxence Vandromme, Julie Jacques, Julien Taillard, Laetitia Jourdan, Clarisse Dhaenens. A scalable biclustering method for heterogeneous medical data. International Workshop on Machine Learning, Optimization and Big Data, Aug 2016, Volterra, Italy. 10122, pp.12, Lecture Notes in Computer Science. 〈hal-01420947〉

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