High Performance Bit Search Mining Technique

Searching algorithms are closely related to the concept of dictionaries. String searching algorithms are too complex in all sorts of applications. To analyze an algorithm is to determine the amount of resources (such as time and storage) necessary to execute it. Most algorithms are designed to work with inputs of arbitrary length. Usually the efficiency or running time of an algorithm is stated as a function relating the input length to the number of steps (time complexity) or storage locations (space complexity). Time efficiency estimates depend on what defined to be all step. For the analysis to correspond usefully to the actual execution time, the time required to perform a step must be guaranteed to be bounded above by a constant. The main objective of this paper is to reduce the scanning the dataset by introducing new searching technique. So far, arrays, trees, hashing, depth first, breadth first, prefix tree based searching are used in association rule mining algorithms. If the size of the input is large, run time analysis of the algorithm is also increased. In this paper, a novel data structure is introduced so that it reduced dataset scan to one search. This new search technique is bit search. This bit search technique is to find the k th itemsets (where k =1,2,3,……n) in one search scan.

[1]  Peng Yi-pu,et al.  Improvement of AprioriTid algorithm for mining association rules , 2005 .

[2]  Gösta Grahne,et al.  Efficiently Using Prefix-trees in Mining Frequent Itemsets , 2003, FIMI.

[3]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[4]  Srinivasan Parthasarathy,et al.  New Algorithms for Fast Discovery of Association Rules , 1997, KDD.

[5]  Johannes Gehrke,et al.  MAFIA: a maximal frequent itemset algorithm for transactional databases , 2001, Proceedings 17th International Conference on Data Engineering.

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

[7]  Devavrat Shah,et al.  Turbo-charging vertical mining of large databases , 2000, SIGMOD '00.

[8]  Wen-Yang Lin,et al.  CBW: an efficient algorithm for frequent itemset mining , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.

[9]  Liu Shuang-ying.  Improvement of AprioriTid Algorithm for Mining Association Rules , 2003 .

[10]  Bart Goethals,et al.  FIMI '03, Frequent Itemset Mining Implementations, Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations, 19 December 2003, Melbourne, Florida, USA , 2003, FIMI.

[11]  Christian Borgelt,et al.  Induction of Association Rules: Apriori Implementation , 2002, COMPSTAT.

[12]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD '00.

[13]  Ming Lei,et al.  A high efficient AprioriTid algorithm for mining association rule , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[14]  Andrea Pietracaprina,et al.  Mining Frequent Itemsets using Patricia Tries , 2003, FIMI.

[15]  Bart Goethals,et al.  Survey on Frequent Pattern Mining , 2003 .

[16]  Hongjun Lu,et al.  H-mine: hyper-structure mining of frequent patterns in large databases , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[17]  Mohammed J. Zaki,et al.  Fast vertical mining using diffsets , 2003, KDD '03.

[18]  Rakesh Agrawal,et al.  Parallel Mining of Association Rules , 1996, IEEE Trans. Knowl. Data Eng..