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Deep Reinforcement Clustering
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DOI：10.1109/TMM.2022.3233249.png)
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摘要
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Deep clustering has attracted plentiful attention in various domains owning to the superior performance. However, the previous deep clustering methods are guided by pre-specified clustering strategies that lack sustained explorations of data structures, degrading recognition of intrinsic patterns hidden in data. To address this challenge, deep reinforcement clustering (DRC) is proposed to learn an adaptive partition policy for pattern mining, which can fully explore structure knowledge of data in an adaptive manner. DRC is defined as a Markov decision process of data partitions, which chooses the optimal cluster prototype for data via maximizing the cumulative reward in state transition of environment. To implement the definition, a Bernoulli action prototype is devised to capture decision distributions in the transition of states, where the heavy-tailed Cauchy distribution precisely measures the structure divergences of data. Furthermore, a reward maximizing policy is designed to guide sustained explorations of data structures, which ensures intra-cluster compactness and inter-cluster separation of data partitions. Finally, extensive experiments are conducted on eight benchmark datasets, and the results demonstrate that DRC outperforms the state-of-the-art baseline methods.
Keyword:
Prototypes
Data mining
Markov processes
Deep learning
Data structures
Neural networks
Clustering methods
Cluster analysis
deep clustering
Markov decision processes
neural networks
reinforcement clustering
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4.6K
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