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Abstract: Mining for association rules between items in a large database of sales transactions has been described as an important database mining problem. In this paper we present an efficient algorithm for mining association rules that is fundamentally different from known algorithms. Compared to the previous algorithms, our algorithm reduces both CPU and I/O overheads. In our experimental study it was found that for large databases, the CPU overhead was reduced by as much as a factor of seven and I/O... (Update)
Context of citations to this paper: More
.... by reducing the number of candidates further [11] the number of transactions to be scanned [4,8,11] or the number of database scans [7,13,14]. TreeProjection [1] is the latest breadth first algorithm. However, breadth first algorithms are inefficient for dense datasets that...
.... cover a broad spectrum of topics including: i) fast algorithms based on the levelwise Apriori framework [3, 13] partitioning [19, 18], and sampling [24] ii) incremental updating and parallel algorithms [6, 2, 8] iii) mining of generalized and multi level rules [21,...
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0.8: Mining for Strong Negative Associations in a Large Database.. - Ashok Savasere (1998) (Correct)
0.6: Fast Algorithms for Mining Association Rules - Agrawal, Srikant (1994) (Correct)
0.3: Online Processing Redux - Hellerstein (Correct)
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0.4: A New Framework For Itemset Generation - Aggarwal, Yu (1998) (Correct)
0.1: Adaptive and Automated Index Selection in RDBMS - Frank, Omiecinski, Navathe (1992) (Correct)
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14: Mining association rules between sets of items in large databases - Agrawal, Imielinski et al. - 1993
13: Discovery of multiple-level association rules from large databases - Han, Fu - 1995
11: Sampling large databases for association rules - Toivonen - 1996
BibTeX entry: (Update)
Ashok Sarasere, Edward Omiecinsky, and Shamkant Navathe. An efficient algorithm for mining association rules in large databases. In 21st Int'l Conf. on Very Large Databases (VLDB), Zurich, Switzerland, Sept. 1995. Also Gatech Technical Report No. GIT-CC-95-04. http://citeseer.ist.psu.edu/sarasere95efficient.html More
@inproceedings{ savasere95efficient,
author = "Ashoka Savasere and Edward Omiecinski and Shamkant B. Navathe",
title = "An Efficient Algorithm for Mining Association Rules in Large Databases",
booktitle = "The {VLDB} Journal",
pages = "432-444",
year = "1995",
url = "citeseer.ist.psu.edu/sarasere95efficient.html" }
Citations (may not include all citations):921 Mining association rules between sets of items in large data.. - Agrawal, Imielinski et al. - 1993 ACM DBLP
298 Parallel database systems: the future of high performance da.. - DeWitt, Gray - 1992 DBLP
208 Fast algorithms for mining association rules in large databa.. (context) - Agrawal, Srikant - 1994 ACM DBLP
160 Knowledge Discovery in Databases (context) - Piatetsky-Shapiro, Frawley - 1991 ACM DBLP
88 Knowledge discovery in databases: an attribute-oriented appr.. - Han, Cai et al. - 1992 DBLP
79 Database systems: achievements and opportunities (context) - Silberschatz, Stonebraker et al. - 1991 ACM DBLP
47 Set-oriented mining of association rules (context) - Houtsma, Swami - 1993
47 Data mining: The search for knowledge in databases - Holsheimer, Siebes - 1993
22 An analysis of three transaction processing architectures (context) - Bhide, Bancilhon et al. - 1988 ACM DBLP
4 IEEE Data Engineering Bulletin (context) - Tsur - 1990
3 Database research at a crossroads: The vienna update (context) - Stonebraker, Agrawal et al. - 1993
3 Knowledge mining in databases: A unified approach through co.. (context) - Anwar, Navathe et al. - 1992
2 Practitioner problems in need of database research (context) - Krishnamurthy, Imielinski - 1991 ACM DBLP
1 Cobinatorial pattern discovery for scientific data: some pre.. (context) - T-L, G-W et al. - 1994
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Technology Overview: A Report on Data Mining - Decker, al. (1995) (Correct)
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