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 implications 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, an association rule based model for discovery of eligibility criteria for jobs is proposed. For this the eligibility requirements of jobs are converted to a set of transactions and then a data base of such transactions is prepared for discovering the association rules in such a way that the antecedent of a rule represents the eligibility requirements and the consequent represents the concerned job. Such rules once discovered can be used for various purposes by the employers, job seekers and the policy makers for proper planning related to recruitment, employment and creation of need based vacancies respectively.

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

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