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.
Highlights
It is observed that there is lack of preparedness among the candidates about extending and maximizing the career opportunities at a very early stage due to ineffective dissemination of knowledge in a suitable manner so that various career options could be evaluated in relation to their future scope, mobility and growth before taking up a particular option
candidates of size k (Ck) = gen_Candidate_Itemsets with the given Lk -1; prune (Ck) ; for all transactions t ε T do increment the count of all candidates in t; Lk = All candidates in Ck with minimum support; k: = k + 1; end; Answer : = Uk Lk; The a priori algorithm is applied on the experimental dataset prepared and various results are obtained
Frequent Itemsets having its last item as job code, the association rules and the Association Rules whose consequent is a job code are discovered from the Job Eligibility Dataset by varying the pre specified minimum support threshold at fixed value of confidence 1% and corresponding graphs are plotted
Summary
A Huge amount of data about career and jobs opportunities are generated and its availability is widely spread out in public domains namely on the internet, news papers, social media and elsewhere. Prospective candidates are required to judiciously analyze such data for selecting better and prosperous career options based on their academic and professional back grounds so as to maximize the scope and growth of their careers. It is observed that there is lack of preparedness among the candidates about extending and maximizing the career opportunities at a very early stage due to ineffective dissemination of knowledge in a suitable manner so that various career options could be evaluated in relation to their future scope, mobility and growth before taking up a particular option This is recognized as a very critical need in the context of the present day as there are diverge and wide ranging career opportunities [1]
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