ACM Transactions on Database Systems (TODS)
Adaptive File Allocation in Star Computer Network
IEEE Transactions on Software Engineering - Special issue on COMPSAC 1982 and 1983
Fast object partitioning using Stochastic learning automata
SIGIR '87 Proceedings of the 10th annual international ACM SIGIR conference on Research and development in information retrieval
Deterministic Learning Automata Solutions to the Equipartitioning Problem
IEEE Transactions on Computers
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
The determination of efficient record segmentations and blocking factors for shared data files
ACM Transactions on Database Systems (TODS)
On self-organizing sequential search heuristics
Communications of the ACM
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Adaptive selection of query processing strategies
Adaptive selection of query processing strategies
A Fixed-Structure Learning Automaton Solution to the Stochastic Static Mapping Problem
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 18 - Volume 19
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Solving multiconstraint assignment problems using learning automata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Solving graph coloring problems using learning automata
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
Combining finite learning automata with GSAT for the satisfiability problem
Engineering Applications of Artificial Intelligence
A fixed structure learning automaton micro-aggregation technique for secure statistical databases
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Service selection in stochastic environments: a learning-automaton based solution
Applied Intelligence
Stochastic Learning for SAT-Encoded Graph Coloring Problems
International Journal of Applied Metaheuristic Computing
| Hi-index | 14.98 |
Modifications to a clustering algorithm in which objects are adaptively partitioned into clusters of equal size are described. The object migration automaton has the advantage of being conceptually simple and easy to implement. Unfortunately, the algorithm may exhibit slow convergence speed and in some cases may not converge at all. The algorithm is modified to provide remedies to these conditions. Through experimental results, the modifications are shown to yield a substantial speedup in convergence (while maintaining 100% accuracy), especially as the number of objects to be partitioned increases.