Fast Object Partitioning Using Stochastic Learning Automata.

B. John Oommen, Daniel C. Y. Ma: Fast Object Partitioning Using Stochastic Learning Automata. SIGIR 1987: 111-122

@inproceedings{DBLP:conf/sigir/OommenM87,
  author    = {B. John Oommen and
               Daniel C. Y. Ma},
  title     = {Fast Object Partitioning Using Stochastic Learning Automata},
  booktitle = {Proceedings of the Tenth Annual International ACM SIGIR Conference
               on Research and Development in Information Retrieval, New Orleans,
               Louisiana, USA, June 3-5, 1987},
  publisher = {ACM},
  year      = {1987},
  pages     = {111-122},
  ee        = {db/conf/sigir/OommenM87.html},
  crossref  = {DBLP:conf/sigir/87},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

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Abstract

Let Omega = {A₁, ..., A_w} be a set of W objects to be partitioned into R classes {P₁, ..., P_R}. 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 automaton, demonstrates an excellent partitioning capability. Experimentally, this solution converges an order of magnitude faster than the best known algorithm in the literature [15,16].

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Proceedings of the Tenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New Orleans, Louisiana, USA, June 3-5, 1987. ACM 1987
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