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Author: search.filters.author.Nawapornanan, ChayananSubject: search.filters.subject.Data mining

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    High candidates generation: A new efficient method for mining share-frequent patterns
    (2017-11-01)
    Nawapornanan, Chayanan
    ;
    Intakosum, Sarun
    ;
    Boonjing, Veera
    The share frequent patterns mining is more practical than the traditional frequent patternset mining because it can reflect useful knowledge such as total costs and profits of patterns. Mining share-frequent patterns becomes one of the most important research issue in the data mining. However, previous algorithms extract a large number of candidate and spend a lot of time to generate and test a large number of useless candidate in the mining process. This paper proposes a new efficient method for discovering share-frequent patterns. The new method reduces a number of candidates by generating candidates from only high transaction-measure-value patterns. The downward closure property of transaction-measure-value patterns assures correctness of the proposed method. Experimental results on dense and sparse datasets show that the proposed method is very efficient in terms of execution time. Also, it decreases the number of generated useless candidates in the mining process by at least 70%.
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    HCG: A new algorithm for mining share-frequent patterns
    (2014-01-01)
    Nawapornanan, Chayanan
    ;
    Boonjing, Veera
    The paper proposes a new efficient algorithm, named HCG algorithm, to mine share-frequent patterns from an incremental pattern set table knowledge called PSTable. The PSTable stores all non redundant patterns with their count information by a single database scan A transaction newly added to the database can be incrementally added to the PSTable. The new algorithm efficiently discovers all share-frequent patterns from the PSTable by generating candidates from only high share atomic patterns. Its correctness is assured by the downward closure property. The experiment results on dense and sparse datasets show that the proposed algorithm is more efficient than existing algorithms in terms of both execution time and number of candidates.
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    An efficient algorithm for mining complete share-frequent itemsets using BitTable and heuristics
    (2012-01-01)
    Nawapornanan, Chayanan
    ;
    Boonjing, Veera
    This paper proposes a new efficient algorithm for mining share-frequent itemsets from BitTable knowledge - extracted once from a transaction database. The knowledge contains sufficient information for such a mining task and provides efficient interactive access. The algorithm finds all share-frequent itemsets by level-wise generating complete promising candidates from a BitTable using heuristics and testing for desired solutions. Simulation results reveal that the proposed algorithm perform significantly better than ShFSM and DCG both runtime and a number of generated candidates. © 2012 IEEE.