A scalable biclustering method for ...
Type de document :
Communication dans un congrès avec actes
Titre :
A scalable biclustering method for heterogeneous medical data
Auteur(s) :
Vandromme, Maxence [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Alicante [Seclin]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Jacques, Julie [Auteur]
Alicante [Seclin]
Taillard, Julien [Auteur]
Alicante [Seclin]
Jourdan, Laetitia [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Dhaenens, Clarisse [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Alicante [Seclin]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Jacques, Julie [Auteur]
Alicante [Seclin]
Taillard, Julien [Auteur]
Alicante [Seclin]
Jourdan, Laetitia [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Dhaenens, Clarisse [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Titre de la manifestation scientifique :
International Workshop on Machine Learning, Optimization and Big Data
Ville :
Volterra
Pays :
Italie
Date de début de la manifestation scientifique :
2016-08-26
Titre de la revue :
Lecture Notes in Computer Science
Discipline(s) HAL :
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]
Résumé en anglais : [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 ...
Lire la suite >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.Lire moins >
Lire la suite >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.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :