Evaluation of Hierarchical Clustering via Markov Decision Processes for Efficient Navigation and Search.
Raúl MorenoWeipéng HuángArjumand YounusMichael P. O'MahonyNeil J. HurleyPublished in: CLEF (2017)
Keyphrases
- markov decision processes
- hierarchical clustering
- state space
- finite state
- dynamic programming
- search algorithm
- clustering method
- optimal policy
- transition matrices
- reachability analysis
- factored mdps
- policy iteration
- search space
- action space
- reinforcement learning
- markov decision process
- state and action spaces
- finite horizon
- partially observable
- reinforcement learning algorithms
- data mining
- clustering algorithm
- k means
- search methods
- hierarchical clustering algorithm
- planning under uncertainty
- decision theoretic planning
- infinite horizon
- action sets
- model based reinforcement learning
- partitional clustering
- average reward
- search strategies
- sufficient conditions
- machine learning