Kreesuradej, Worapoj

Loading...
Thumbnail Image
Preferred name
Kreesuradej, Worapoj
Alternative Name
Kreesuradej, W.
Main Affiliation
Email
worapoj.kr@kmitl.ac.th

Settings

Now showing 1 - 4 of 4
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Mining dynamic databases using probability-based incremental association rule discovery algorithm
    (2009-11-20)
    Amornchewin, Ratchadaporn
    ;
    Kreesuradej, Worapoj
    In dynamic databases, new transactions are appended as time advances. This paper is concerned with applying an incremental association rule mining to extract interesting information from a dynamic database. An incremental association rule discovery can create an intelligent environment such that new information or knowledge such as changing customer preferences or new seasonal trends can be discovered in a dynamic environment. In this paper, probability-based incremental association rule discovery algorithm is proposed to deal with this problem. The proposed algorithm uses the principle of Bernoulli trials to find expected frequent itemsets. This can reduce a number of times to scan an original database. This paper also proposes a new updating and pruning algorithm that guarantee to find all frequent itemsets of an updated database efficiently. The simulation results show that the proposed algorithm has better performance than that of previous work. © J.UCS.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Incremental association rule mining using promising frequent itemset algorithm
    (2007-12-01)
    Amornchewin, Ratchadaporn
    ;
    Kreesuradej, Worapoj
    Association rule discovery is an important area of data mining. In dynamic databases, new transactions are appended as time advances. This may introduce new association rules and some existing association rules would become invalid. Thus, the maintenance of association rules for dynamic databases is an important problem. In this paper, promising frequent itemset algorithm, which is an incremental algorithm, is proposed to deal with this problem. The proposed algorithm uses maximum support count of 1-itemsets obtained from previous mining to estimate infrequent itemsets, called promising itemsets, of an original database that will capable of being frequent itemsets when new transactions are inserted into the original database. Thus, the algorithm can reduce a number of times to scan the original database. As a result, the algorithm has execution time faster than that of previous methods. This paper also conducts simulation experiments to show the performance of the proposed algorithm. The simulation results show that the proposed algorithm has a good performance. ©2007 IEEE.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    False positive item set algorithm for incremental association rule discovery
    (2009-12-01)
    Amornchewin, Ratchadaporn
    ;
    Kreesuradej, Worapoj
    In a dynamic database where the new transaction are inserted into the database, keeping patterns up-to-date and discovering new pattern are challenging problems of great practical importance. This may introduce new association rules and some existing association rules would become invalid. Thus, the maintenance of association rules for dynamic database is an important problem. In this paper, false positive itemset algorithm, which is an incremental algorithm, is proposed to deal with this problem. The proposed algorithm uses maximum support count of 1-itemsets obtained from previous mining to estimate infrequent itemsets, called false positive itemsets, of an original database. False positive itemsets will capable of being frequent itemsets when new transactions are inserted into an original database. This can reduce a number of times to scan an original database. This paper also proposes a new updating and pruning algorithm that guarantee to find all frequent itemsets of an updated database efficiently. The simulation results show that the proposed algorithm has a good performance.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Probability-based incremental association rule discovery algorithm
    (2008-11-28)
    Amornchewin, Ratchadaporn
    ;
    Kreesuradej, Worapoj
    In dynamic databases, new transactions are appended as time advances. This may introduce new association rules and some existing association rules would become invalid. Thus, the maintenance of association rules for dynamic databases is an important problem. In this paper, probability-based incremental association rule discovery algorithm is proposed to deal with this problem. The proposed algorithm uses the principle of Bernoulli trials to find expected frequent itemsets. This can reduce a number of times to scan an original database. This paper also proposes a new updating and pruning algorithm that guarantee to find all frequent itemsets of an updated database efficiently. The simulation results show that the proposed algorithm has a good performance. © 2008 IEEE.