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Fourth IEEE International Conference on Data Mining (ICDM'04)
Mining Associations by Linear Inequalities
Brighton, United Kingdom
November 01-November 04
ISBN: 0-7695-2142-8
| ASCII Text | x | ||
| Tsay Young Lin, "Mining Associations by Linear Inequalities," Data Mining, IEEE International Conference on, pp. 154-161, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004. | |||
| BibTex | x | ||
| @article{ 10.1109/ICDM.2004.10098, author = {Tsay Young Lin}, title = {Mining Associations by Linear Inequalities}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2004}, isbn = {0-7695-2142-8}, pages = {154-161}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10098}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Data Mining, IEEE International Conference on TI - Mining Associations by Linear Inequalities SN - 0-7695-2142-8 SP154 EP161 A1 - Tsay Young Lin, PY - 2004 KW - association KW - deduction KW - feature KW - granules KW - bitmaps VL - 0 JA - Data Mining, IEEE International Conference on ER - | |||
The main theorem is: Generalized associations of a relational table can be found by a finite set of linear inequalities within polynomial time. It is derived from the following three results, which were established in ICDM0'02 and are re-developed here. They are (1) Isomorphic Theorem: Isomorphic relations have isomorphic patterns. Such an isomorphism classifies relational tables into isomorphic classes. (2) A variant of the classical bitmaps indexes uniquely exists in each isomorphic class. We take it as the canonical model of the class. (3) All possible attributes/features can be generated by a generalized procedure of the classical AOG (attribute oriented generalization). Then, (4) the main theorem for canonical model is established. By isomorphism theorem, we had the final result (5).
Index Terms:
association, deduction, feature, granules, bitmaps
Citation:
Tsay Young Lin, "Mining Associations by Linear Inequalities," icdm, pp.154-161, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004
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