A scalable biclustering method for ...
Document type :
Communication dans un congrès avec actes
Title :
A scalable biclustering method for heterogeneous medical data
Author(s) :
Vandromme, Maxence [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Alicante [Seclin]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Jacques, Julie [Auteur]
Alicante [Seclin]
Taillard, Julien [Auteur]
Alicante [Seclin]
Jourdan, Laetitia [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Dhaenens, Clarisse [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Alicante [Seclin]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Jacques, Julie [Auteur]
Alicante [Seclin]
Taillard, Julien [Auteur]
Alicante [Seclin]
Jourdan, Laetitia [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Dhaenens, Clarisse [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Conference title :
International Workshop on Machine Learning, Optimization and Big Data
City :
Volterra
Country :
Italie
Start date of the conference :
2016-08-26
Journal title :
Lecture Notes in Computer Science
HAL domain(s) :
Informatique [cs]
Informatique [cs]/Recherche d'information [cs.IR]
Informatique [cs]/Algorithme et structure de données [cs.DS]
Informatique [cs]/Recherche d'information [cs.IR]
Informatique [cs]/Algorithme et structure de données [cs.DS]
English abstract : [en]
We define the problem of biclustering on heterogeneous data,that is, data of various types (binary, numeric, etc.). This problem hasnot yet been investigated in the biclustering literature.We propose a newmethod, HBC ...
Show more >We define the problem of biclustering on heterogeneous data,that is, data of various types (binary, numeric, etc.). This problem hasnot yet been investigated in the biclustering literature.We propose a newmethod, HBC (Heterogeneous BiClustering), designed to extract biclus-ters from heterogeneous, large-scale, sparse data matrices. The goal ofthis method is to handle medical data gathered by hospitals (on patients,stays, acts, diagnoses, prescriptions, etc.) and to provide valuable insighton such data. HBC takes advantage of the data sparsity and uses a con-structive greedy heuristic to build a large number of possibly overlappingbiclusters. The proposed method is successfully compared with a stan-dard biclustering algorithm on small-size numeric data. Experiments onreal-life data sets further assert its scalability and efficiency.Show less >
Show more >We define the problem of biclustering on heterogeneous data,that is, data of various types (binary, numeric, etc.). This problem hasnot yet been investigated in the biclustering literature.We propose a newmethod, HBC (Heterogeneous BiClustering), designed to extract biclus-ters from heterogeneous, large-scale, sparse data matrices. The goal ofthis method is to handle medical data gathered by hospitals (on patients,stays, acts, diagnoses, prescriptions, etc.) and to provide valuable insighton such data. HBC takes advantage of the data sparsity and uses a con-structive greedy heuristic to build a large number of possibly overlappingbiclusters. The proposed method is successfully compared with a stan-dard biclustering algorithm on small-size numeric data. Experiments onreal-life data sets further assert its scalability and efficiency.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
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