Algorithm and Performance Evaluation of Adaptive Multidimensional Clustering Technique.

Shinya Fushimi, Masaru Kitsuregawa, Masaya Nakayama, Hidehiko Tanaka, Tohru Moto-Oka: Algorithm and Performance Evaluation of Adaptive Multidimensional Clustering Technique. SIGMOD Conference 1985: 308-318

@inproceedings{DBLP:conf/sigmod/FushimiKNTM85,
  author    = {Shinya Fushimi and
               Masaru Kitsuregawa and
               Masaya Nakayama and
               Hidehiko Tanaka and
               Tohru Moto-Oka},
  editor    = {Shamkant B. Navathe},
  title     = {Algorithm and Performance Evaluation of Adaptive Multidimensional
               Clustering Technique},
  booktitle = {Proceedings of the 1985 ACM SIGMOD International Conference on
               Management of Data, Austin, Texas, May 28-31, 1985},
  publisher = {ACM Press},
  year      = {1985},
  pages     = {308-318},
  ee        = {http://doi.acm.org/10.1145/318898.318928, db/conf/sigmod/FushimiKNTM85.html},
  crossref  = {DBLP:conf/sigmod/85},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

This paper proposes the multidimensional clustering technique called GKD-tree method which is fully adaptive to both of tuple and query distributions. The method is based on the theoretical analysis of the performance of multldimensional clustering. Its algorithm and performance are described. It is shown that GKD tree method can largely reduce the average number of page accesses. For several distributions its behavior is analyzed and the modification to the algorithm is suggested to further improve the performance. GKD tree method was frst developed as the physical database organization of the relational database machine GRACE. The implementation issues of GKD-tree method in the database machine are also discussed.

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References

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