On the complexity of cooperative solution concepts
Mathematics of Operations Research
Coalition, cryptography, and stability: mechanisms for coalition formation in task oriented domains
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Data mining: concepts and techniques
Data mining: concepts and techniques
A Microeconomic View of Data Mining
Data Mining and Knowledge Discovery
Multi-agent algorithms for solving graphical games
Eighteenth national conference on Artificial intelligence
A linear approximation method for the Shapley value
Artificial Intelligence
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Collaborative clustering with background knowledge
Data & Knowledge Engineering
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Multilingual sentence categorization and novelty mining
Information Processing and Management: an International Journal
Using coalitional games to detect communities in social networks
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
A game theory based approach for community detection in social networks
BNCOD'13 Proceedings of the 29th British National conference on Big Data
Coalition formation based on marginal contributions and the Markov process
Decision Support Systems
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Abstract: Realistic objects are not only described by their own attributes, but also described by their mutual relationships in a specific domain. By mainly considering the mutual associations among the given objects, in this paper we propose a method for multi-objective categorization based on the game theory and Markov process. We adopt Shapley value in coalitional games to measure the player's satisfaction degree in a group. We then give the concept of priority groups and an algorithm to combine small-size priority groups to large-size ones, and thus the efficiency of calculating the players' satisfaction degree can be improved. We further define a improving-replay Markov process to model the process of forming a reasonable payoff configuration. Accordingly, we give a simulation algorithm to obtain the desired payoff configuration to categorize players into groups by their satisfaction degrees. Finally, we give experimental results and performance studies to verify the efficiency and effectiveness of our methods.