Main Article Content

Pankaj Kumar Deva Sarma


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.


Download data is not yet available.

Article Details

Author Biography

Pankaj Kumar Deva Sarma, Department of Computer Science, Assam University, SILCHAR, ASSAM PIN 788 011

Pankaj Kumar Deva Sarma is a Ph. D. in Computer Science from Gauhati University, India in Data Mining and Knowledge Discovery. He received the B.Sc (Honours) and M. Sc. Degrees in Physics from the University of Delhi, Delhi, India before receiving the Post Graduate Diploma in Computer Application and the M. Tech degree in Computer Science from New Delhi, India. He is currently an associate professor of Computer Science in the University Department of Computer Science at the Assam University, Silchar, India. His primary research interest is in algorithms, data base systems, data mining and knowledge discovery, parallel and distributed computing, artificial intelligence and neural network. He is life member of ISTE and IETE, New Delhi.


P. K. Deva Sarma, “An Association Rule Based Model for Discovery of Eligibility Criteria for Jobsâ€, International Journal of Computer Sciences and Engineering, Vol. 6, No. 2, pp. 143-149, 2018.

Han, J, and Kamber, M, “Data Mining: Concepts and Techniquesâ€, Morgan Kaufmann, San Fransisco, USA, 2011

R. Agrawal, T. Imielinsky, and A. Swami, “Mining Association Rules Between Sets of Items in Large Databasesâ€, Proceedings of ACM SIGMOD Intl. Conference on Management of Data, pp. 207 -216, USA, 1993.

P. K. Deva Sarma, and A. K. Mahanta, “Reduction of Number of Association Rules with Inter Itemset Distance in Transaction Databasesâ€, International Journal of Database Management Systems (IJDMS), Vol. 4, No. 5, pp. 61 – 82, 2012.

R. Agrawal, T. Imielinsky, and A. Swami, “Database Mining: A performance perspectiveâ€, IEEE Transactions on Knowledge and Data Engineering, Vol. 5, pp. 914 – 925, 1993.

R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules in Large Databasesâ€, Proceedings of 20th International Conference on Very Large Databases, pp. 487 – 499, Santiago, Chile, 1994.

S. Brin, R. Motwani, and C. Silverstein, “Beyond Market Baskets: Generalizing Association Rules to Correlations†Proceedings of the ACM International Conference on Management of Data, pp. 265 -276, 1997.

L. Wang, and C. Yi, “Application of Association Rules Mining In Employment Guidanceâ€, Advanced Materials Research Vols. 479-481, pp 129-132.

A. Gupta, and D. Garg, “Applying Data Mining Techniques in Job Recommender System for Considering Candidate Job Preferencesâ€, In the Proceedings of International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1458- 1465, 2014.

C. Chien, and L. Chen, “Data Mining to Improve Personnel Selection and Enhance Human Capital: A Case Study in High-Technology Industryâ€, Expert Systems with Applications, Vol. 34, No. 1, pp. 280-290, 2008.

B. Jantawan, and C. Tsai, “The Application of Data Mining to Build Classification Model for Predicting Graduate Employmentâ€, International Journal of Computer Science and Information Security, 2013

T. Mishra, D. Kumar, and S. Gupta, “Students’ Employability Prediction Model through Data Miningâ€, International Journal of Applied Engineering Research, Volume 11, Number 4, pp 2275-2282, 2016.

M. Sapaat, and A. Mustapha, J. Ahmad, K. Chamili, and R. Muhamad, “A Data Mining Approach to Construct a Graduates Employability Model in Malaysia, †International Journal of New Computer Architectures and their Applications, Vol. 1, No. 4, pp. 1086-1098, 2011.

I. A. Wowczko, “Skills and Vacancy Analysis with Data Mining Techniquesâ€, Informatics, Vol. 2, pp. 31-49, 2015.

N. N. Salvithal, and R.B. Kulkarni, “Appraisal Management System using Data mining Classification Techniqueâ€, International Journal of Computer Applications, Volume 135, No.12, 2016.

A. Savasere, E. Omiecinski, and S. Navathe, “An Efficient Algorithm for Mining Association Rules in Large Databasesâ€, Proceedings of the 21st International Conference on Very Large Data Bases (VLDB), pp. 432-444, Zurich, Switzerland, 1995.

S. S. Yen and A. L. P. Chen, “An Efficient Approach to Discovering Knowledge from Large Databasesâ€, Fourth International Conference on Parallel and Distributed Information Systems, 1996.

S. Brin, R. Motowani, J. D. Ullman and S. Tsur, “Dynamic Itemset Counting and Implication Rules for Market Basket Dataâ€, Proceedings of the ACM SIGMOD International Conference on Management of Data, 1997.

J.L. Lin and M. H. Dunham, “Mining Association Rules: Anti Skew Algorithms,†14th International Conference on Data Engineering, 1998.

Lin D.I., and Kedem Z. M., “Pincer –Search: A New Algorithm for Discovering Maximal Frequent Setâ€, Sixth International Conference on Extending Database Technology, 1998.

D. Gunopulos, H. Mannila, and S. Saluja, “Discovering All the Most Specific Sentences by Randomized Algorithmsâ€, International Conference on Database Theory, 1997.

R. J. Bayardo, “Efficiently Mining Long Patterns from Databasesâ€, Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 85–93, USA, 1998.

J. Han, J. Pei, and Y. Yin, “Mining Frequent Patterns without Candidate Generationâ€, Proceedings of the ACM SIGMOD International Conference on Management of Data, USA, 2000.

M. J. Zaki, “Scalable Algorithms for Association Miningâ€, IEEE Transactions on Knowledge and Data Engineering, Vol. 12, No 3, pp. 372 – 390, 2000.

Rakesh Agrawal, “Parallel Mining of Association Rulesâ€, IEEE Transcations on Knowledge and Data Engineering, Vol. 8, No 6, pp. 962-969, 1996.

H. Toivonen, “Sampling Large Databases for Association Rulesâ€, Proceedings, 22nd Conference, very Large Databases, 1996.

P. K. Deva Sarma, A. K. Mahanta, “An Apriori Based Algorithm to Mine Association Rules with Inter Itemset Distanceâ€, International Journal of Data Mining and Knowledge Management Process (IJDKP), Vol. 3, No. 6, pp. 73 – 94, November 2013.

M. Hahsler, C. Buchta, and K. Hornik, “Selective Association Rule Generation,†Computational Statistic, vol. 23, no. 2, pp. 303-315, Kluwer Academic Publishers, 2008.

F. Guillet and H. Hamilton, Quality Measures in Data Mining, Springer, 2007.

Claudia Marinica and Fabrice Guillet, “Knowledge-Based Interactive Postmining of Association Rules using Ontologiesâ€, IEEE Transactions on Knowledge and Data Eng., vol. 22, No. 6, pp. 784 – 797, 2010.