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Pincer Search: A New Algorithm for Discovering the Maximum Frequent Set.

Dao-I Lin, Zvi M. Kedem: Pincer Search: A New Algorithm for Discovering the Maximum Frequent Set. EDBT 1998: 105-119
@inproceedings{DBLP:conf/edbt/LinK98,
  author    = {Dao-I Lin and
               Zvi M. Kedem},
  editor    = {Hans-J{\"o}rg Schek and
               F{\`e}lix Saltor and
               Isidro Ramos and
               Gustavo Alonso},
  title     = {Pincer Search: A New Algorithm for Discovering the Maximum Frequent
               Set},
  booktitle = {Advances in Database Technology - EDBT'98, 6th International
               Conference on Extending Database Technology, Valencia, Spain,
               March 23-27, 1998, Proceedings},
  publisher = {Springer},
  series    = {Lecture Notes in Computer Science},
  volume    = {1377},
  year      = {1998},
  isbn      = {3-540-64264-1},
  pages     = {105-119},
  ee        = {db/conf/edbt/LinK98.html, http://link.springer.de/link/service/series/0558/bibs/1377/13770105.htm},
  crossref  = {DBLP:conf/edbt/98},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
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References

[1]
Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami: Mining Association Rules between Sets of Items in Large Databases. SIGMOD Conference 1993: 207-216 BibTeX
[2]
Rakesh Agrawal, Ramakrishnan Srikant: Fast Algorithms for Mining Association Rules in Large Databases. VLDB 1994: 487-499 BibTeX
[3]
Rakesh Agrawal, John C. Shafer: Parallel Mining of Association Rules. IEEE Trans. Knowl. Data Eng. 8(6): 962-969(1996) BibTeX
[4]
Sergey Brin, Rajeev Motwani, Jeffrey D. Ullman, Shalom Tsur: Dynamic Itemset Counting and Implication Rules for Market Basket Data. SIGMOD Conference 1997: 255-264 BibTeX
[5]
Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, Ramasamy Uthurusamy (Eds.): Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press 1996, ISBN 0-262-56097-6
Contents BibTeX
[6]
Dimitrios Gunopulos, Heikki Mannila, Sanjeev Saluja: Discovering All Most Specific Sentences by Randomized Algorithms. ICDT 1997: 215-229 BibTeX
[7]
Jiawei Han, Yongjian Fu: Discovery of Multiple-Level Association Rules from Large Databases. VLDB 1995: 420-431 BibTeX
[8]
...
[9]
Heikki Mannila, Hannu Toivonen, A. Inkeri Verkamo: Discovering Frequent Episodes in Sequences. KDD 1995: 210-215 BibTeX
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Heikki Mannila, Hannu Toivonen: Levelwise Search and Borders of Theories in Knowledge Discovery. Data Min. Knowl. Discov. 1(3): 241-258(1997) BibTeX
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...
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Tom M. Mitchell: Generalization as Search. Artif. Intell. 18(2): 203-226(1982) BibTeX
[13]
Banu Özden, Sridhar Ramaswamy, Abraham Silberschatz: Cyclic Association Rules. ICDE 1998: 412-421 BibTeX
[14]
Jong Soo Park, Ming-Syan Chen, Philip S. Yu: An Effective Hash Based Algorithm for Mining Association Rules. SIGMOD Conference 1995: 175-186 BibTeX
[15]
Gregory Piatetsky-Shapiro: Discovery, Analysis, and Presentation of Strong Rules. Knowledge Discovery in Databases 1991: 229-248 BibTeX
[16]
Ashok Savasere, Edward Omiecinski, Shamkant B. Navathe: An Efficient Algorithm for Mining Association Rules in Large Databases. VLDB 1995: 432-444 BibTeX
[17]
Ramakrishnan Srikant, Rakesh Agrawal: Mining Generalized Association Rules. VLDB 1995: 407-419 BibTeX
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Hannu Toivonen: Sampling Large Databases for Association Rules. VLDB 1996: 134-145 BibTeX
[19]
Mohammed Javeed Zaki, Srinivasan Parthasarathy, Mitsunori Ogihara, Wei Li: New Algorithms for Fast Discovery of Association Rules. KDD 1997: 283-286 BibTeX

Referenced by

  1. Nicolas Pasquier, Yves Bastide, Rafik Taouil, Lotfi Lakhal: Discovering Frequent Closed Itemsets for Association Rules. ICDT 1999: 398-416
  2. Roberto J. Bayardo Jr.: Efficiently Mining Long Patterns from Databases. SIGMOD Conference 1998: 85-93
  3. Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopulos, Prabhakar Raghavan: Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. SIGMOD Conference 1998: 94-105
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