An Algorithm for Frequent Pattern Mining Based On Apriori

Frequent pattern mining is a heavily researched area in the field of data mining with wide range of applications. Mining frequent patterns from large scale databases has emerged as an important problem in data mining and knowledge discovery community. A number of algorithms has been proposed to determine frequent pattern. Apriori algorithm is the first algorithm proposed in this field. With the time a number of changes proposed in Apriori to enhance the performance in term of time and number of database passes. In this paper three different frequent pattern mining approaches (Record filter, Intersection and Proposed Algorithm) are given based on classical Apriori algorithm. In these approaches Record filter approach proved better than classical Apriori Algorithm, Intersection approach proved better than Record filter approach and finally proposed algorithm proved that it is much better than other frequent pattern mining algorithm. In last we perform a comparative study of all approaches on dataset of 2000 transaction.

[1]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[2]  Qingguo Zheng,et al.  When to Update the Sequential Patterns of Stream Data? , 2002, PAKDD.

[3]  Mohammed J. Zaki,et al.  Incremental and interactive mining for frequent itemsets in evolving databases , 2003 .

[4]  Philip S. Yu,et al.  Mining concept-drifting data streams using ensemble classifiers , 2003, KDD '03.

[5]  Roberto J. Bayardo,et al.  Efficiently mining long patterns from databases , 1998, SIGMOD '98.

[6]  Li Yang,et al.  Mining Short Association Rules with One Database Scan , 2004, IKE.

[7]  Simon Fraser.  MULTI-DIMENSIONAL SEQUENTIAL PATTERN MINING , 2001 .

[8]  Ho Tu Bao,et al.  A DISTRIBUTED ALGORITHM FOR MINING ASSOCIATION RULES , 2007 .

[9]  Charu C. Aggarwal,et al.  A Tree Projection Algorithm for Generation of Frequent Item Sets , 2001, J. Parallel Distributed Comput..

[10]  Srinivasan Parthasarathy,et al.  Incremental and interactive sequence mining , 1999, CIKM '99.

[11]  R. Agarwal.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[12]  Dennis Shasha,et al.  StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time , 2002, VLDB.

[13]  Srinivasan Parthasarathy,et al.  Parallel and Distributed Frequent Itemset Mining on Dynamic Datasets , 2003, HiPC.

[14]  Philip S. Yu,et al.  Resource-Aware Mining with Variable Granularities in Data Streams , 2004, SDM.

[15]  Hongjun Lu,et al.  False Positive or False Negative: Mining Frequent Itemsets from High Speed Transactional Data Streams , 2004, VLDB.