KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Nawapornanan, ChayananSubject: search.filters.subject.Share-frequent patterns mining

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    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%.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    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.