Patent
Frequent Pattern Mining
Shi Han,Yingnong Dang,Dongmei Zhang,Song Ge +3 more
- 27 Apr 2011
128
TL;DR: This comprehensive reference consists of 18 chapters from prominent researchers in the field of frequent pattern mining, and contains a survey describing key research on the topic, a case study and future directions.
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Abstract: A system for frequent pattern mining uses two layers of processing: a plurality of computing nodes, and a plurality of processors within each computing node. Within each computing node, the data set against which the frequent pattern mining is to be performed is stored in shared memory, accessible concurrently by each of the processors. The search space is partitioned among the computing nodes, and sub-partitioned among the processors of each computing node. If a processor completes its sub-partition, it requests another sub-partition. The partitioning and sub-partitioning may be performed dynamically, and adjusted in real time.
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Citations
•Posted Content
A Survey on Session-based Recommender Systems
TL;DR: A systematic and comprehensive review on SBRS is provided and a hierarchical framework is created to categorize the related research issues and methods of SBRS and to reveal its intrinsic challenges and complexities.
385
Frequent itemset mining: A 25 years review
TL;DR: This work analyzes how this task has been considered during the last decades by considering centralized systems as well as parallel (shared or nonshared memory) architectures and solutions can be divided into exhaustive search and nonexhaustive search models.
217
A structured view on pattern mining-based biclustering
TL;DR: A structured and integrated view of the contributions of state-of-the-art PM-based biclustering approaches is proposed, a set of principles for a guided definition of new PM- based bic Lustering approaches are made available, and their relevance for applications in pattern recognition is discussed.
79
Mining frequent itemsets in a stream
Toon Calders,Nele Dexters,Bart Goethals +2 more
- 01 Jan 2007
TL;DR: In this paper, the problem of finding frequent itemsets in a continuous stream of transactions is studied and an incremental algorithm that allows, at any time, to immediately produce the current frequencies of all frequent item sets is proposed.
76
Factors influencing the patterns of wrong-way driving crashes on freeway exit ramps and median crossovers: exploration using 'Eclat' association rules to promote safety
TL;DR: The results of this study confirmed that WWD fatalities are more likely to be associated with head-on collisions and fatal WWD crashes tend to be involved with male drivers and off-peak hours.
74
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TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
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Rakesh Agrawal,Tomasz Imielinski,Arun N. Swami +2 more
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TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
•Proceedings Article
Fast Algorithms for Mining Association Rules in Large Databases
Rakesh Agrawal,Ramakrishnan Srikant +1 more
- 12 Sep 1994
TL;DR: Two new algorithms for solving thii problem that are fundamentally different from the known algorithms are presented and empirical evaluation shows that these algorithms outperform theknown algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems.
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