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Abstract: The task of mining association rules consists of two main steps. The first involves finding the set of all frequent itemsets. The second step involves testing and generating all high confidence rules among itemsets. In this paper we show that it is not necessary to mine all frequent itemsets in the first step, instead it is sufficient to mine the set of closed frequent itemsets, which is much smaller than the set of all frequent itemsets. It is also not necessary to mine the set of all... (Update)
Context of citations to this paper: More
.... of events in a sliding time window [13] There are algorithms that do not rely on candidate generation (e.g. FP growth [8] CHARM [21], and GenMax [20] Given that the OSSM created with mmin segments will consume too much space, in Section 5 we consider the...
...patterns are long, mining FI is infeasible because of the exponential number of frequent itemsets. Thus, algorithms mining FCI [9,15,10] are proposed since FCI is enough to generate association rules. However, FCI could also be exponentially large as the FI. As a result,...
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BibTeX entry: (Update)
M.J. Zaki and C. Hsiao. Charm: an efficient algorithm for closed association rule mining. Tech. Report., RPI, 1999. http://citeseer.ist.psu.edu/zaki99charm.html More
@misc{ zaki99charm,
author = "M. Zaki and C. Hsiao",
title = "Charm: an efficient algorithm for closed association rule mining",
text = "M.J. Zaki and C. Hsiao. Charm: an efficient algorithm for closed association
rule mining. Tech. Report., RPI, 1999.",
year = "1999",
url = "citeseer.ist.psu.edu/zaki99charm.html" }
Citations (may not include all citations):509 Introduction to Lattices and Order (context) - DaveyandH, Priestley - 1990
400 Fast discovery of association rules (context) - Agrawal, Mannila et al. - 1996
242 Dynamic itemset counting and implication rules for market ba.. - Brin, Motwani et al. - 1997
164 An efficient algorithm for mining association rules in large.. (context) - Savasere, Omiecinski et al. - 1995
129 Formal Concept Analysis: Mathematical Foundations (context) - Ganter, Wille - 1999
125 An effective hash based algorithm for mining association rul.. - Park, Chen et al. - 1995
109 New algorithms for fast discovery of association rules - Zaki, Parthasarathy et al. - 1997
108 Efficiently mining long patterns from databases (context) - Bayardo - 1998
85 Discovering frequent closed itemsets for association rules - Pasquier, Bastide et al. - 1999
54 Pincer-search: A new algorithm for discovering the maximum f.. - Lin, Kedem - 1998
44 Incremental concept formation algorithms based on galois - Godin, Missaoui et al. - 1991
39 IEEE Transactions on Knowledge and Data Engineering (context) - Zaki, for - 2000
37 Discovering all the most specific sentences by randomized al.. - Gunopulos, Mannila et al. - 1997
34 Database architecture optimized for the new bottleneck: Memo.. - Manegold, Boncz et al. - 1999
26 Mining association rules: Anti-skew algorithms - Lin, Dunham - 1998
16 Integrating association rule mining with databases: alternat.. (context) - Sarawagi, Thomas et al. - 1998
16 Theoretical foundations of association rules - Zaki, Ogihara - 1998
16 Implications partielles dans un contexte (context) - Luxenburger - 1991
14 Familles minimales d'implications informatives resultant d'u.. (context) - Guigues, Duquenne - 1986
The graph only includes citing articles where the year of publication is known.
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