Data Mining Made Simple by A priori Algorithm for Market Analysis

Abstract

Data mining and data warehousing are increasingly becoming popular among IT professionals, academics and researchers from different disciplines. Business enterprises, small, medium or large scale, are considering the deployment of a warehouse as a major step and as a matter of pride. Data mining or knowledge discovery in data bases combines the techniques from mathematics, statistics, algorithms and artificial intelligence to extract the knowledge. Data mining is a main phase of knowledge discovery in databases (KDD) for extracting the knowledge based on the patterns and their correlation by the application of appropriate association rules to the information available from the data set. The outcome of KDD is used to analyze or predict on the future aspects in any area of consideration. The authors made an analysis and prediction of the market sales based on the historical information from the databases by considering the items information at different levels to generate the association rules. The widely used algorithm in data mining, i.e., a priori algorithm is specifically considered for the extraction of knowledge.

Keywords: Association rules, knowledge base, expert system, itemsets, candidate itemsets