Open AccessProceedings Article
CMRULES: An Efficient Algorithm for Mining Sequential Rules Common to Several Sequences
Philippe Fournier-Viger,Usef Faghihi,Roger Nkambou,Engelbert Mephu Nguifo +3 more
- 06 May 2010
TL;DR: This work proposes CMRULES, an algorithm for mining sequential rules common to many sequences in sequence databases –not for mining rules appearing frequently in sequences, which is more efficient for low support thresholds, and has a better scalability.
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Abstract: We propose CMRULES, an algorithm for mining sequential rules common to many sequences in sequence databases –not for mining rules appearing frequently in sequences. For this reason, the algorithm does not use a sliding-window approach. Instead, it first finds association rules to prune the search space for items that occur jointly in many sequences. Then it eliminates association rules that do not meet minimum confidence and support thresholds according to the time ordering. We evaluated the performance of CMRULES in three different ways. First, we provide an analysis of its time complexity. Second, we compared its performance on a public dataset with a variation of an algorithm from the literature. Results show that CMRULES is more efficient for low support thresholds, and has a better scalability. Lastly, we report a real application of the algorithm in a complex system.
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Citations
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TL;DR: In this article, the authors provide a brief review and analysis of the current status of frequent pattern mining and discuss some promising research directions, including a comparative study between the described approaches.
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Learning task models in ill-defined domain using an hybrid knowledge discovery framework
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A computational model for causal learning in cognitive agents
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References
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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.
Mining sequential patterns
Rakesh Agrawal,Ramakrishnan Srikant +1 more
- 06 Mar 1995
TL;DR: Three algorithms are presented to solve the problem of mining sequential patterns over databases of customer transactions, and empirically evaluating their performance using synthetic data shows that two of them have comparable performance.
On Knowledge Discovery and Data Mining
Daniel A. Keim
- 01 Jan 1997
TL;DR: The whole activity is not a sequential process.
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The Apriori Algorithm – a Tutorial
Markus Hegland,John Dedman,M. Hegland +2 more
- 01 Jan 2005
TL;DR: Basic concepts of association rule discovery are reviewed including support, confidence, the apriori property, constraints and parallel algorithms.
120
Discovering Sequential Association Rules with Constraints and Time Lags in Multiple Sequences
Sherri K. Harms,Jitender S. Deogun,Tsegaye Tadesse +2 more
- 27 Jun 2002
TL;DR: The experimental results validate the superior performance of the method for efficiently finding relationships between global climatic episodes and local drought conditions and compare the new approach to existing methods and show how they complement each other to discover associations in a drought risk management decision support system.
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