@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} }BibTeX

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
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its date appear, and notice is given that copying
is by the permission of the Very Large Data Base
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Contents BibTeX

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- Jeff Edmonds, Jarek Gryz, Dongming Liang, Renée J. Miller: Mining for Empty Rectangles in Large Data Sets. ICDT 2001: 174-188
- Richard T. Snodgrass: Review - Semantic Compression and Pattern Extraction with Fascicles. ACM SIGMOD Digital Review 2: (2000)

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