W. Pijls and J.C. Bioch. Mining frequent itemsets in memory-resident databases. In E. Postma and M. Gyssens, editors, Proceedings of the Eleventh Belgium-Netherlands Conference on Artificial Intelligence (BNAIC1999. Home/Search Document Details and Download
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Faster Association Rules for Multiple Relations - Siegfried Nijssen Joost (2001) (3 citations) (Correct)
....it is performed reasonably fast. Much research has been done in order to develop efficient algorithms. A wellknown algorithm resulting from this research is APRIORI, of which many variants have been developed, such as APRIORITID [Agrawal et al. 1996] and a breadth first algorithm introduced by Pijls and Bioch [1999] . On the other hand, efforts have been done to extend the usability of association rules beyond the basic case of basket analysis. Dehaspe and De Raedt [1997] use the notion of atom sets as a first order logic extension of item sets. The incorporation of techniques from Inductive Logic ....
....notions introduced in the WARMR algorithm. The fourth section introduces our modifications, which are verified by giving results of experiments in the fifth section. The sixth section concludes. 2 Breadth first APRIORI Our algorithm is based on a variation of APRIORI that was introduced by Pijls and Bioch [1999] . The algorithm performs the same task as APRIORI. Given a database D which contains subsets T of a set of items I = fi 1 ; i 2 ; i m g the algorithm discovers all frequent item sets, which are the subsets I I for which support(I ) j fT 2 D j I Tg j exceeds a predefined threshold. ....
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W. Pijls and J. C. Bioch. Mining frequent itemsets in memory-resident databases. In E. Postma, editor, Proceedings Eleventh Belgium /Netherlands Artificial Intelligence Conference, pages 75--82, 1999.
APRIORI, A Depth First Implementation - Kosters, Pijls Self-citation (Pijls) (Correct)
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W. Pijls and J.C. Bioch. Mining frequent itemsets in memory-resident databases. In E. Postma and M. Gyssens, editors, Proceedings of the Eleventh Belgium-Netherlands Conference on Artificial Intelligence (BNAIC1999.
A Theoretical and Practical Comparison - Of Depth First Self-citation (Pijls) (Correct)
....frequent itemsets in large databases. We describe the algorithms in a similar style, derive theoretical formulas, and provide experiments on both synthetic and real life data to illustrate the theory. 1 Introduction We examine the theoretical and practical complexity of Depth First (DF , see [5]) and FP growth (FP , see [3] implementations of Apriori (see [1] two of the fastest known data mining algorithms to nd frequent itemsets in large databases. There exist many implementations of Apriori (see, e.g. 4, 6] We would like to focus on algorithms that assume that the whole ....
....recursion tree) the node 2 68 would also contain all 68 transactions that have 2, restricted to the products 3, 4 and 5. An FP tree is built that eciently contains all these transactions, and subdatabases are derived for the recursive steps. More details can be either found in the original papers [5, 3] or in the following sections. We will give a theoretical basis for the analysis of the two algorithms. We have chosen for a somewhat informal presentation of the algorithms, intertwined with our analysis. We will present practical results, and we mention several diculties. Indeed, FP is a ....
W. Pijls and J.C. Bioch. Mining frequent itemsets in memory-resident databases. In E. Postma and M. Gyssens, editors, Proceedings of the Eleventh Belgium-Netherlands Conference on Arti cial Intelligence (BNAIC1999.
Classification and Target Group Selection based upon Frequent .. - Pijls, Potharst Self-citation (Pijls) (Correct)
....data set D which acts as training set. The collection of frequent patterns in D is constructed and is stored into a trie T . Any algorithm designed to nd frequent patterns is appropriate. Apriori is the best known algorithm in this area. Another algorithm is the depth rst algorithm presented in [7]. We assume that supp(P ) and also supp c (P ) for every c is stored into the cell at the end of path P . To calculate these quantities, only a slight extension of Apriori is needed. The classi cation of a record R proceeds as follows. Each path or equivalently each frequent pattern P is examined ....
W. Pijls and J.C. Bioch, Mining frequent itemsets in memory-resident databases, in: E. Postma et al. (eds), Proceedings Eleventh Belgium /Netherlands Articial Intelligence Conference (BNAIC 99), pp 75-82, 1999.
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