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Browsing by Author "Seno, Masakazu"

Now showing 1 - 4 of 4
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    Finding Frequent Patterns Using Length-Decreasing Support Constraints
    (2003-01-27) Seno, Masakazu; Karypis, George
    Finding prevalent patterns in large amount of data has been one of the major problems in the area of data mining. Particularly, the problem of finding frequent itemset or sequential patterns in very large databases has been studied extensively over the years, and a variety of algorithms have been developed for each problem. The key feature in most of these algorithms is that they use a constant support constraint to control the inherently exponential complexity of these two problems. In general,patterns that contain only a few items will tend to be interesting if they have a high support, whereas long patterns can still be interesting even if their support is relatively small. Ideally, we want to find all the frequent patterns whose support decreases as a function of theirlength without having to find many uninteresting infrequent short patterns. Developing such algorithms is particularly challenging because the downward closure property of the constant support constraint cannot be used to prune short infrequent patterns. In this paper we present two algorithms, LPMiner and SLPMiner. Given alength-decreasing support constraint, LPMiner finds all the frequentitemset patterns from an itemset database, and SLPMiner finds all thefrequent sequential patterns from a sequential database. Each of thesetwo algorithms combines a well-studied efficient algorithm forconstant-support-based pattern discovery with three effective databasepruning methods that dramatically reduce the runtime. Our experimentalevaluations show that both LPMiner and SLPMiner, by effectively exploitingthe length-decreasing support constraint, are up to two orders of magnitudefaster, and their runtime increases gradually as the average length ofthe input patterns increases.
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    LPMiner: An Algorithm for Finding Frequent Itemsets Using Length-Decreasing Support Constraint
    (2001-06-19) Seno, Masakazu; Karypis, George
    Over the years, a variety of algorithms for finding frequent itemsets in very large transaction databases have been developed. The key feature in most of these algorithms is that they use a constant support constraint to control the inherently exponential complexity of the problem. Ingeneral, itemsets that contain only a few items will tend to be interesting if they have a high support, whereas long itemsets can still be interesting even if their support is relatively small. Ideally, we desire to have an algorithm that finds all the frequent itemsets whose support decreasesas a function of their length. In this paper we present an algorithm called LPMiner, that finds all itemsets that satisfy a length-decreasing support constraint. Our experimental evaluation shows that LPMiner is up to two orders of magnitude faster than the FP-growth algorithm forfinding itemsets at a constant support constraint, and that its runtime increases gradually as the average length of the transactions (and the discovered itemsets) increases.
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    PAFI: A Pattern Finding Toolkit
    (2003-07-07) Seno, Masakazu; Kuramochi, Michihiro; Karypis, George
    N/A
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    SLPMiner: An Algorithm for Finding Frequent Sequential Patterns Using Length-Decreasing Support Constraint
    (2002-06-05) Seno, Masakazu; Karypis, George
    Over the years, a variety of algorithms for finding frequent sequential patterns in very large sequential databases have been developed. The key feature in most of these algorithms is that they use a constant support constraint to control the inherently exponential complexity of the problem. In general, patterns that contain only a few items will tend to be interesting if they have a high support, whereas long patterns can still be interestingeven if their support is relatively small. Ideally, we desire to have an algorithm that finds all the frequent patterns whose support decreases as a function of their length. In this paper we present an algorithm called SLPMiner, that finds all sequential patterns that satisfya length-decreasing support constraint. SLPMiner combines an efficient database-projection-based approach forsequential pattern discovery with three effective database pruning methods that dramatically reduce the search space. Our experimental evaluation shows that SLPMiner, by effectively exploiting the length-decreasing support constraint, is up to two orders of magnitude faster, and its runtime increases gradually as the average length ofthe sequences (and the discovered frequent patterns increases.