Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal, "Mining Frequent Patterns with Counting Inference," SIGKDD Explorations, vol. 2, no. 2, Dec. 2000. Home/Search Document Details and Download
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This paper is cited in the following contexts:
Frequency-Based Views to Pattern Collections - Mielikäinen (2003) (Correct)
....a condensed representation for the collection of the frequent patterns with discretized frequencies. Condensed representations frequent patterns are structures from which the set of frequent patterns can be inferred. There has been many studies on condensed representations of frequent patterns [2, 7, 8, 11, 12, 28, 29, 31, 35, 38, 39, 42, 46, 49] The condensed representations of the frequent patterns rely on regularity properties of the pattern collection and its frequencies. For example, an especially popular condensed representation for frequent sets called closed frequent sets is based on the observation that there are exact ....
Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhai, Mining frequent patterns with counting inference, SIGKDD Explorations, 2 (2000), pp. 66--75.
Optimization of Association Rule Mining Queries - Jeudy, Boulicaut (2002) (1 citation) (Correct)
....it is desirable that Step 1 and Step 1 of this strategy are done more eciently than the direct generation of frequent itemsets with Apriori. Several algorithms exist that uses various condensed representations of frequent itemsets: Close [22] Closet[24] Charm [27] Min Ex [4, 6] or Pascal [3]. These algorithms provide di erent condensed representations : frequent closed itemsets (Close, Closet, Charm) frequent free itemsets (or key patterns) Min Ex, Pascal) or frequent free itemsets (Min Ex) These algorithms enable tractable frequent itemset extractions from dense and ....
Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal. Mining frequent patterns with counting inference. SIGKDD Explorations, 2(2):66-75, Dec. 2000.
Mining All Non-Derivable Frequent Itemsets - Calders, Goethals (2002) (29 citations) (Correct)
....threshold is set too low, or the data is highly correlated, the number of frequent itemsets itself can be prohibitively large. To overcome this problem, recently several proposals have been made to construct a concise representation of the frequent itemsets, instead of mining all frequent itemsets [13, 3, 6, 5, 14, 15, 7, 11]. Our contributions The main goal of this paper is to present several new methods to identify redundancies in the set of all frequent itemsets and to exploit these redundancies, resulting in a concise representation of all frequent itemsets and signi cant performance improvements of a mining ....
.... We present connections between our proposal and recent proposals for concise representations, such as free sets [6] disjunction free sets [7] and closed sets [13] We also show that known tricks to improve performance of frequent itemset mining algorithms, such as used in MAXMINER [4] and PASCAL [3], can be described in our framework. 5. We present several experiments on real life datasets that show the e ectiveness of the deduction rules. 2 The outline of the paper is as follows. In Section 2 we introduce the deduction rules. Section 3 describes how we can use the rules to reduce the set ....
[Article contains additional citation context not shown here]
Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal. Mining frequent patterns with counting inference. ACM SIGKDD Explorations, 2(2):66-74, 2000.
Optimization of Association Rule Mining Queries - Jeudy, Boulicaut (2002) (1 citation) (Correct)
....it is desirable that Step 1 and Step 1 of this strategy are done more e ciently than the direct generation of frequent itemsets with Apriori. Several algorithms exist that uses various condensed representations of frequent itemsets: Close [22] Closet[24] Charm [27] Min Ex [4, 6] or Pascal [3]. These algorithms provide di erent condensed representations : frequent closed itemsets (Close, Closet, Charm) frequent free itemsets (or key patterns) Min Ex, Pascal) or frequent itemsets (Min Ex) These algorithms enable tractable frequent itemsets extractions from dense and ....
Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal. Mining frequent patterns with counting inference. SIGKDD Explorations, 2(2):6675, Dec. 2000.
CHARM: An Efficient Algorithm for Closed Itemset Mining - Zaki, Hsiao (2002) (17 citations) (Correct)
....fewer than all frequent patterns. While mining maximal sets help understand the long patterns in dense domains, they lead to a loss of information; since subset frequency is not available maximal sets are not suitable for generating rules. The second is to mine only the frequent closed sets [3, 14, 15, 18]. Closed sets are lossless in the sense that they uniquely determine the set of all frequent itemsets and their exact frequency. At the same time closed sets can themselves be orders of magnitude smaller than all frequent sets, especially on dense databases. Our past work [18] addressed the ....
....The current trend toward large (gigabyte sized) main memories, combined with the above features, makes CHARM a practical and ecient algorithm for reasonably large databases. We compare CHARM against previous methods for mining closed sets such as Close [14] Closet [15] Ma a [6] and Pascal [3]. Extensive experiments con rm that CHARM provides signi cant improvement over existing methods for mining closed itemsets, for both dense as well as sparse datasets. 2 Frequent Pattern Mining Let I be a set of items, and D a database of transactions, where each transaction has a unique identi ....
[Article contains additional citation context not shown here]
Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal. Mining frequent patterns with counting inference. SIGKDD Explorations, 2(2), December 2000.
Conceptual Clustering with Iceberg Concept Lattices - Stumme, Taouil, Bastide.. (2001) (1 citation) Self-citation (Bastide Taouil Stumme Lakhal) (Correct)
.... Discovery in Databases (KDD) Here FCA has been used as a method for conceptual clustering [38, 7, 12, 35, 23] a formal framework for implication and association rules discovery and reduction [19, 26, 4, 31] and for improving the response times of algorithms for mining association rules [25, 26, 5]. The interaction of FCA and KDD in general has been discussed in [33] and [14] In this paper we present a new approach of conceptual clustering with FCA: iceberg concept lattices. Iceberg concept lattices show only the topmost part of a concept lattice. The extensions of the concepts provide ....
Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal. Mining frequent patterns with counting inference. Sigkdd Explorations, 2(2):71--80, 2000. Special Issue on Scalable Algorithms.
Efficient Algorithms for Mining Closed - Itemsets And Their (Correct)
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Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal, "Mining Frequent Patterns with Counting Inference," SIGKDD Explorations, vol. 2, no. 2, Dec. 2000.
Unknown - Sec Minimum Support (Correct)
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Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal. Mining frequent patterns with counting inference. SIGKDD Explorations, 2(2), Dec. 2000.
Unknown - Framework For Mining (Correct)
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Y. Bastide et al. Mining frequent patterns with counting inference. SIGKDD Explorations, 2(2), Dec. 2000.
BLOSOM: A Framework for Mining Arbitrary - Boolean Expressions Over (Correct)
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Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal. Mining frequent patterns with counting inference. SIGKDD Explorations, 2(2), Dec. 2000.
Implicit Enumeration of Patterns - Mielikäinen (2004) (Correct)
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Bastide, Y., Taouil, R., Pasquier, N., Stumme, G., Lakhai, L.: Mining frequent patterns with counting inference. SIGKDD Explorations 2 (2000) 66--75
Change Profiles - Mielikäinen (Correct)
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Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhai. Mining frequent patterns with counting inference. SIGKDD Explorations, 2(2):66--75, 2000.
Adaptive and Resource-Aware Mining of Frequent Sets - Orlando Palmerini Perego (2002) (7 citations) (Correct)
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Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal. Mining frequent patterns with counting inference. ACM SIGKDD Explorations Newsletter, 2(2):66--75, December 2000.
kDCI: a Multi-Strategy Algorithm for Mining Frequent Sets - Claudio Lucchese Salvatore (Correct)
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Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal. Mining frequent patterns with counting inference. ACM SIGKDD Explorations Newsletter, 2(2):66-- 75, December 2000.
Advances in Frequent Itemset Mining Implementations: Report.. - Goethals, Zaki (2003) (1 citation) (Correct)
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Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal. Mining frequent patterns with counting inference. SIGKDD Explorations, 2(2), Dec. 2000.
kDCI: a Multi-Strategy Algorithm for Mining Frequent Sets - Claudio Lucchese Salvatore (Correct)
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Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal. Mining frequent patterns with counting inference. ACM SIGKDD Explorations Newsletter, 2(2):66-- 75, December 2000.
FIMI'03: Workshop on Frequent Itemset Mining Implementations - Goethals, Zaki (2003) (1 citation) (Correct)
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Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal. Mining frequent patterns with counting inference. SIGKDD Explorations, 2(2), Dec. 2000.
Using Transposition for Pattern Discovery - From Microarray Data (Correct)
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Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal. Mining frequent patterns with counting inference. SIGKDD Explorations, 2(2):66 -- 75, Dec. 2000.
Maximal-Kosarak - Ibe Mafia Eclat (Correct)
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Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal. Mining frequent patterns with counting inference. SIGKDD Explorations, 2(2), Dec. 2000.
Real World Association Rule Mining - Adriano Alonso Veloso (Correct)
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Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, L. Lakhal. Mining Frequent Patterns with Counting Inference. SIGKDD Explorations, December 2000.
Minimal k-Free Representations of Frequent Sets - Calders, Goethals (Correct)
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Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal. Mining frequent patterns with counting inference. SIGKDD Explorations, 2(2):66--75, 2000.
Change Profiles - Mielikäinen (Correct)
No context found.
Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhai. Mining frequent patterns with counting inference. SIGKDD Explorations, 2(2):66--75, 2000.
Adaptive and Resource-Aware Mining of Frequent Sets - Orlando Palmerini Perego (2002) (7 citations) (Correct)
No context found.
Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal. Mining frequent patterns with counting inference. ACM SIGKDD Explorations Newsletter, 2(2):66--75, December 2000.
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