Semantic Compression and Pattern Extraction with Fascicles.

@inproceedings{DBLP:conf/vldb/JagadishMN99,
  author    = {H. V. Jagadish and
               J. Madar and
               Raymond T. Ng},
  editor    = {Malcolm P. Atkinson and
               Maria E. Orlowska and
               Patrick Valduriez and
               Stanley B. Zdonik and
               Michael L. Brodie},
  title     = {Semantic Compression and Pattern Extraction with Fascicles},
  booktitle = {VLDB'99, Proceedings of 25th International Conference on Very
               Large Data Bases, September 7-10, 1999, Edinburgh, Scotland,
               UK},
  publisher = {Morgan Kaufmann},
  year      = {1999},
  isbn      = {1-55860-615-7},
  pages     = {186-198},
  ee        = {db/conf/vldb/JagadishMN99.html},
  crossref  = {DBLP:conf/vldb/99},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

Often many recoords in a database share similar values for several attributes. If one is able to identify and group together records that share similar values for some - even if not all - attributes, one can both obtain a more parsimonious representation of the data, and gain useful insight into the data from a mining perspective.

In this paper, we introduce the notion of fascicles. A fascicle F(k,t) is a subset of records that have k compact attributes. An attribute A of a collection F of records is compact if the width of the range of A-values (for numeric attributes) or the number of distinct A-values (for categorial attributes) of all the records in F does not exceed t. We introduce and study two problems related to fascicles. First, we consider how to find fascicles such that the total storage of the relation is minimized. Second, we study how best to extract fascicles whose sizes exceed a given minimum threshold (i.e., support) and that represent patterns of maximal quality, where quality is measured by the pair (k,t). We develop algorithms to attack both of the above problems. We show that these two problems are very hard to solve optimally. But we demonstrate empirically that good solutions can be obtained using our algorithms.

Copyright © 1999 by the VLDB Endowment. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by the permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.

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