Browse > Article

Association Rule Discovery using TID List Table  

Chai, Duck-Jin (전남대학교 전산학과)
Hwang, Bu-Hyun (전남대학교 전산학과)
Publication Information
Journal of KIISE:Databases / v.32, no.3, 2005 , pp. 219-227 More about this Journal
Abstract
In this paper, we propose an efficient algorithm which generates frequent itemsets by only one database scanning. A frequent itemset is subset of an itemset which is accessed by a transaction. For each item, if informations about transactions accessing the item are exist, it is possible to generate frequent itemsets only by the extraction of items haying an identical transaction ID. Proposed method in this paper generates the data structure which stores transaction ID for each item by only one database scanning and generates 2-frequent itemsets by using the hash technique at the same time. k(k$\geq$3)-frequent itemsets are simply found by comparing previously generated data structure and transaction ID. Proposed algorithm can efficiently generate frequent itemsets by only one database scanning .
Keywords
Data mining; Association rules; Frequent itemsets;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 R. Agrawal, T. Imielinski and A. Swami, 'Mining association rules between sets of items in large database,' In ACM SIGMOD, pp.207-216, 1993   DOI
2 박종수, 유원경, 홍기형 '연관 규칙 탐사와 그 응용', 한국 정보과학회지 제16권 제 9호, pp. 37-44, 1998   과학기술학회마을
3 A. savasere, E. Omiecinski and S. Navathe, 'An efficient algorithm for mining association rules in large databases,' In VLDB, pp. 432-443, 1995
4 R. Srikant, Q. Vu and R. Agrawal, 'Mining association rules with item constraints,' In KDD, pp. 67-73, 1997
5 S. Sarawagi, S. Thomas and R. Agrawal, 'Integrating association rule mining with relational database systems: Alternatives and implications,' In SIGMOD, pp. 343-354, 1998   DOI   ScienceOn
6 R. Agarwal, C. Aggarwal and V. V. V. Prasad, 'A tree projection algorithm for generation of frequent itemsets,' In J. Parallel and Distributed Computing, 2000   DOI   ScienceOn
7 M. Klemettinen, h. Mannila, P. Ronkainen, h. Toivonen and A.I. Verkamo, 'Finding interesting rules from large sets of discovered association rules,' In CIKM, pp. 401-408, 1994   DOI
8 J. Han, J. Pei and Y. Yin, 'Mining frequent patterns without candidate generation,' In ACM SIGMOD, pp. 1-12, 2000   DOI
9 G. Grahne, L. Lakshmanan and X. Wang, 'Efficient mining of constrained correlated sets,' In ICDE, 2000   DOI
10 R. Ng, L. V. S. Lakshmanan, J. Han and A. Pang, 'Exploratory mining and pruning optimizations of constrained associations rules,' In SIGMOD, pp. 13-24, 1998   DOI
11 B. Liu, W. Hsu and Y. Ma, 'Mining association rules with multiple minimum supports,' In ACM SIGKDD, pp. 337-341, 1999   DOI
12 B. Lent, A. Swami and J. Widom, 'Clustering association rules,' In ICDE, pp. 220-231, 1997   DOI
13 J.S. Park, M.S. Chen and P.S. Yu, 'An effective hash-based algorithm for mining association rules,' In ACM SIGMOD, pp.175-186, 1995   DOI   ScienceOn
14 R. Agrawal and R. Srikant, 'Fast algorithms for mining association rules,' In VLDB, pp. 487-499, 1994