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Improvement in Apriori Algorithm with New Parameters

Journal: International Journal of Science and Research (IJSR) (Vol.3, No. 9)

Publication Date:

Authors : ; ;

Page : 1395-1399

Keywords : Data Mining; KDD; Association Rule Mining; Apriori; Market Basket Analysis; Support; Confidence; Profit; Weight; Q-factor; PW-Factor;

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Data Mining or Knowledge Discovery in Databsaes is an advanced approach which refers to the extraction of previously unknown and useful information from large databases. Association Rule Mining is an important technique of data mining. This technique emphasis on finding interesting relationships. For understanding these relationships, a technique called Market Basket Analysis has been introduced in Data Mining. This helps in understanding the customer behaviour more easily so that frequent patterns can be generated. Apriori algorithm is used in association rule mining for generating frequent patterns. But it generates patterns only on the basis of presence and absence of items, resulting into lack of efficient results. So new parameters have been included in this paper which will be helpful in giving maximum profit to the business organizations. This paper shows that how addition of new parameters improve the efficiency of Apriori algorithm by comparing the results of improved algorithm with the results of traditional Apriori algorithm.

Last modified: 2021-06-30 21:07:44