DISCOVERY AND ANALYSIS OF JOB ELIGIBILITY AS ASSOCIATION RULES BY APRIORI ALGORITHM

Pankaj Kumar Deva Sarma

Abstract


Association rule mining is a data mining technique in which pattern of occurrences of one set of items with another set of items in databases of transactions are discovered as rules of implication with certain measures of interestingness. Support or the frequency of occurrences of sets of items and confidence are the most widely used measures of interestingness of association rules of the form X→Y where X and Y are disjoint sets of items. Though the problem of association rule mining emerged from analysis of market basket data in supermarket there are numerous areas of applications of association rule mining technique. In this paper, association rule mining method is applied to discover and analyze eligibility criteria for jobs from a large set of data for choosing career and professional goals effectively. For this the data are collected by conducting a wide survey and is prepared and modeled suitably. Then the a priori algorithm is implemented for discovering the frequent itemsets and the association rules. The discovered rules are then classified based on the kind of jobs and also based on the kinds of qualifications. The discovered results are analyzed and interpreted and the computational performances are also analyzed.

Keywords


Data mining, big data, association rule, support, confidence, classification, employment analytic

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References


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DOI: https://doi.org/10.26483/ijarcs.v9i2.5650

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