ACM Transactions on Database Systems (TODS)
Absorbing and ergodic discretized two-action learning automata
IEEE Transactions on Systems, Man and Cybernetics
Deterministic Learning Automata Solutions to the Equipartitioning Problem
IEEE Transactions on Computers
A clustering algorithm for hierarchical structures
ACM Transactions on Database Systems (TODS)
Precision Weighting—An Effective Automatic Indexing Method
Journal of the ACM (JACM)
Index selection in a self-adaptive data base management system
SIGMOD '76 Proceedings of the 1976 ACM SIGMOD international conference on Management of data
Learning Algorithms Theory and Applications
Learning Algorithms Theory and Applications
A heuristic approach to attribute partitioning
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
An approximation algorithm for a file-allocation problem in a hierarchical distributed system
SIGMOD '80 Proceedings of the 1980 ACM SIGMOD international conference on Management of data
Object partitioning by using learning automata
Object partitioning by using learning automata
Dynamic information and library processing
Dynamic information and library processing
Improvements to an Algorithm for Equipartitioning
IEEE Transactions on Computers
Cluster characterization in information retrieval
SAC '93 Proceedings of the 1993 ACM/SIGAPP symposium on Applied computing: states of the art and practice
Efficient fast learning automata
Information Sciences—Informatics and Computer Science: An International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Let &OHgr; = {A1, …, AW} be a set of W objects to be partitioned into R classes {P1, …, PR}. The objects are accessed in groups of unknown size and the size of these groups need not be equal. Additionally, the joint access probabilities of the objects are unknown. The intention is that the objects accessed more frequently together are located in the same class. This problem has been shown to be NP-hard [15, 16]. In this paper, we propose two stochastic learning automata solutions to the problem. Although the first one is relatively fast, its accuracy is not so remarkable in some environments. The second solution, which uses a new variable structure stochastic automation, demonstrates an excellent partitioning capability. Experimentally, this solution converges an order of magnitude faster than the best known algorithm in the literature [15, 16].