Unsupervised collaborative boosting of clustering: An unifying framework for multi-view clustering, multiple consensus clusterings and alternative clustering

Autor: Jacques-Henri Sublemontier
Přispěvatelé: CA, Laboratoire d'Informatique Fondamentale d'Orléans (LIFO), Ecole Nationale Supérieure d'Ingénieurs de Bourges-Université d'Orléans (UO)-Ecole Nationale Supérieure d'Ingénieurs de Bourges-Université d'Orléans (UO), IEEE - INNS, Sublemontier, Jacques-Henri
Rok vydání: 2013
Předmět:
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]
Clustering high-dimensional data
Boosting (machine learning)
Fuzzy clustering
Computer science
Correlation clustering
[INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]
Conceptual clustering
ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.11: Distributed Artificial Intelligence
02 engineering and technology
[INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]
computer.software_genre
Machine learning
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Biclustering
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
CURE data clustering algorithm
020204 information systems
Consensus clustering
0202 electrical engineering
electronic engineering
information engineering
Cluster analysis
Brown clustering
business.industry
ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.3: Clustering
[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]
Hierarchical clustering
ComputingMethodologies_PATTERNRECOGNITION
Canopy clustering algorithm
FLAME clustering
Unsupervised learning
Affinity propagation
020201 artificial intelligence & image processing
Data mining
Artificial intelligence
Hierarchical clustering of networks
business
computer
ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning
Zdroj: IJCNN
International Joint Conference on Neural Networks (IJCNN 2013)
International Joint Conference on Neural Networks (IJCNN 2013), IEEE-INNS, Aug 2013, Dallas, United States
DOI: 10.1109/ijcnn.2013.6706911
Popis: International audience; In this paper, we propose a collaborative framework that is able to solve multi-view and alternative clustering problems using some clustering ensemble and semi-supervised clustering principles. We provide a mechanism to control, via a information sharing model, different clustering algorithms to obtain consensus or alternative clustering solutions. The strong point is that our approach does not need to know which clustering algorithms to use nor their parameters
Databáze: OpenAIRE
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