Kshitij Tayal
Editorial Notes
NOTICE OF CONCERN: ACM has received evidence that casts doubt on the integrity of the peer review process for the ICIA 2016 Conference. As a result, ACM is issuing a Notice of Concern for all papers published and strongly suggests that the papers from this Conference not be cited in the literature until ACM's investigation has concluded and final decisions have been made regarding the integrity of the peer review process for this Conference.
ABSTRACT
Association and classification are two important tasks in data mining. Literature abounds with works that unify these two techniques. This paper presents a new algorithm called Particle Swarm Optimization trained Classification Association Rule Mining (PSOCARM) for associative classification that generates class association rules (CARs) from transactional database by formulating a combinatorial global optimization problem, without having to specify minimal support and confidence unlike other conventional associative classifiers. We devised a new rule pruning scheme in order to reduce the number of rules and increasing the generalization aspect of the classifier. We demonstrated its effectiveness for phishing email and phishing website detection. Our experimental results indicate the superiority of our proposed algorithm with respect to accuracy and the number of rules generated as compared to the state-of-the-art algorithms.
- Abdelhamid, N., Ayesh, A., Thabtah, F., Ahmadi, S., and Hadi, W. 2012. MAC: A multiclass associative classification algorithm. Journal of Information & Knowledge Management. 11, 02 (2012).Google Scholar
Cross Ref
- Abdelhamid, N., Ayesh, A., and Thabtah. F. 2013. Associative Classification Mining for Website Phishing classification. In Proceedings of the ICAI (Las Vegas, USA 2013).Google Scholar
- Abdelhamid, N., Ayesh, A., and Thabtah. F. 2014. Phishing detection based Associative Classification data mining. Expert Systems with Applications. 41, 13 (2014), 5948--5959.Google ScholarCross Ref
- Abdelhamid, N. and Thabtah, F. 2014. Associative Classification Approaches: Review and Comparison. Journal of Information & Knowledge Management. 13, 03 (2014).Google ScholarCross Ref
- Aburrous, M., Hossain, M. A., Dahal, K., and Thabtah, F. 2010. Associative classification techniques for predicting e-Banking phishing websites. In Proceeding of Multimedia Computing and Information Technology (MCIT), (IEEE International Conference, Sharjah, 2010), 9--12.Google Scholar
- Agrawal, R. and Srikant, R. 1994. Fast algorithms for mining association rules. In Proceedings of 20th int. conf. very large data bases, VLDB (Santiago de Chile, Chile, 1215, 1994). Google ScholarDigital Library
- Ajlouni, M., Iqbal, AL., Hadi, W., and Alwedyan, J. 2013. Detecting phishing websites using associative classification. European Journal of Business and Management. 5, 15 (2013), 36--40.Google Scholar
- Antonie, M-L. and Zaïane, O. R. 2002. Text document categorization by term association. In Proceedings of IEEE International Conference on Data Mining (San Jose, California, USA, 2002). Google ScholarDigital Library
- Antonie, M-L. and Zaïane, O. R. 2004. An associative classifier based on positive and negative rules. In Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery (Paris, France, 2004). Google ScholarDigital Library
- Baralis, E., Chiusano, S., and Garza, P. 2004. On support thresholds in associative classification. In Proceedings of the ACM symposium on Applied computing. (Nicosia, Cyprus, 2004), 553--558. Google ScholarDigital Library
- Baralis, E. and Garza, P. 2002. A lazy approach to pruning classification rules.Data Mining, In Proceedings IEEE International Conference. (2002), 35--42. Google ScholarDigital Library
- Chien, C., Ya-Wen, and Chen, Yen-Liang. 2010. Mining associative classification rules with stock trading data--A GA-based method. Knowledge-Based Systems. 23, 6(2010), 605--614 Google ScholarDigital Library
- Coenen, F. 2004. LUCS KDD implementation of CBA (Classification Based on Associations). http://www.csc.liv.ac.uk/~frans/KDD/Software/CMAR/cba.html, Department of Computer Science, The University of Liverpool, UK, 2004.Google Scholar
- Coenen, F. 2004. LUCS KDD implementation of CMAR (Classification based on Multiple Association Rules). http://www.csc.liv.ac.uk/~frans/KDD/Software/CMAR/cmar.html, Department of Computer Science, The University of Liverpool, UK, 2004.Google Scholar
- Coenen, F. 2004. LUCS-KDD implementations of CPR (Classification based on Predictive AssociationRules), http://www.cxc.liv.ac.uk/~frans/KDD/Software/FOIL_PRM_CPAR/cpar.html, Department of Computer Science, The University of Liverpool, UK, 2004.Google Scholar
- Coenen, F. 2004. LUCS-KDD implementations of FOIL (First Order Inductive Learner), http://www.cxc.liv.ac.uk/~frans/KDD/Software/FOIL_PRM_CPAR/foil.html, Department of Computer Science, The University of Liverpool, UK, 2004.Google Scholar
- Coenen, F. 2004. LUCS-KDD implementations of PRM (Predictive Rule Mining), http://www.cxc.liv.ac.uk/~frans/KDD/Software/FOIL_PRM_CPAR/prm.html, Department of Computer Science, The University of Liverpool, UK, 2004.Google Scholar
- Damodaram, R. and Valarmathi, M. 2011. Phishing website detection using particle swarm optimization. Int. J. Comput. Sci. Security. 5, 5 (2011), 477--490.Google Scholar
- Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., and Stützle, Th. 2006. Ant Colony Optimization and Swarm Intelligence. In Proceedings of 5th International Workshop, ANTS (Brussels, Belgium, September 4-7, 2006). 4150, (2006). Google ScholarDigital Library
- Eberhart, R.C. and Kennedy, J. 1995. A new optimizer using particle swarm theory. In Proceedings of the sixth international symposium on micro machine and human science (Nagoya, Japan, October, 1995). 1, 39--43.Google ScholarCross Ref
- Han, J., Kamber, M., and Pei, J. 2006. Data mining: Concepts and techniques. Morgan Kaufmann, (2006). Google ScholarDigital Library
- Han, J., Pei, J., and Yin, Y. 2000. Mining frequent patterns without candidate generation. ACM SIGMOD Record (Dallas, Texas, USA). 29, 2 (2000). Google ScholarDigital Library
- Kennedy, J. and Eberhart, R.C. 1997. A discrete binary version of the particle swarm algorithm,. In Systems, Man, and Cybernetics (IEEE International Conference in October, 1997, Orlando, FL). 5, 4104--4108.Google Scholar
- Kundu, G., Islam, Md. M., Munir, S., Bari, Md. F. 2008. ACN: An associative classifier with negative rules. In Proceedings of 11th IEEE International Conference on Computational Science and Engineering, (Sao Paulo, 2008). 369--375 Google ScholarDigital Library
- Li, W., Han, J., and Pei, J. 2001. CMAR: Accurate and efficient classification based on multiple class-association rules. In Proceedings IEEE International Conference on Data Mining (San Jose, CA, 2001). Google ScholarDigital Library
- Liu, B., Hsu, W., and Ma, Y. 1998. Integrating Classification and Association Rule Mining. In Proceedings KDD, (New York, 27-31 August, 1998). AAAI, 80--86. Google ScholarDigital Library
- Liu, B., Ma, Y., and Wong, C-K. 2001. Classification using association rules: weaknesses and enhancements. Data mining for scientific and engineering applications. Springer US. (2001). 591--605.Google Scholar
- Mangat, V. and Vig, R. 2014. Dynamic PSO-Based Associative Classifier for Medical Datasets. IETE Technical Review. 31,4 (2014), 258--265.Google ScholarCross Ref
- Phishing Corpus, http://monkey.org/~jose/wiki/doku.php?id=PhishingCorpus, last accessed: June, 2012.Google Scholar
- Pandey, M., and Ravi, V. 2012. Detecting phishing e-mails using text and data mining. In Proceeding of Computational Intelligence & Computing Research (ICCIC) (IEEE International Conference, December 2012, Coimbatore, India). 1--6.Google Scholar
- Pandey, M., and Ravi, V. 2013. Text and Data Mining to Detect Phishing Websites and Spam Emails.In Proceedings of Swarm, Evolutionary, and Memetic Computing (Chennai, India, Springer 2013). 559--573. Google ScholarDigital Library
- Quinlan, J. R. 1993. C4. 5: programs for machine learning. Morgan kaufmann, 1, (1993). Google ScholarDigital Library
- Quinlan, J. R. and Cameron-Jones, R. M. 1993. FOIL: A Midterm Report. In Proceedings of ECML (Vienna, Austria, 1993). 3--20. Google ScholarDigital Library
- Rapid Miner, http://www.rapid-i.com, last accessed: May, 2012.Google Scholar
- Sarath, K. N. V. D. and Ravi, V. 2013. Association rule mining using binary particle swarm optimization. Engineering Applications of Artificial Intelligence, 26, 8 (2013), 1832--1840. Google ScholarDigital Library
- Shahzad, W. and Baig, A. 2011. Hybrid associative classification algorithm using ant colony optimization. International Journal of Innovative Computing, Information and Control. 7,12 (2011), 6815--6826.Google Scholar
- Spam Assassin, http://www.spamassassin.apache.org, last accessed: June, 2012.Google Scholar
- Thabtah, F. 2005. Cowling, P., and Peng, Y. 2005. MCAR: multi-class classification based on association rule, In Proceedings of Computer Systems and Applications, The 3rd ACS/IEEE International Conference (2005), Cairo, Egypt, 33. Google ScholarDigital Library
- Thabtah, F. 2007. A review of associative classification mining. The Knowledge Engineering Review, Cambridge University Press. 22,01 (2007) 37--65. Google ScholarDigital Library
- Wur, S-Y., Leu, Y. 1999. An effective Boolean algorithm for mining association rules in large databases, In Proceedings of Database Systems for Advanced Applications (6th International Conference Hsinchu, 1999). 179--186. Google ScholarDigital Library
- Xiaoyuan, X., Han, G., and Min, H. 2004. A novel algorithm for associative classification of image blocks. In Proceedings of the Fourth IEEE International Conference on Computer and Information Technology (Wuhan, China, 2004). Google ScholarDigital Library
- Yin, X. and Han, J. 2003. CPAR: Classification based on Predictive Association Rules. In Proceedings of SIAM Int. Conf. on Data Mining (San Fransisco, CA, 2003). SDM'03. 331--335.Google Scholar
Recommendations
Weighted association rule mining using particle swarm optimization
PAKDD'11: Proceedings of the 15th international conference on New Frontiers in Applied Data MiningAssociation rule mining is an important data mining task that discovers relationships among items in a transaction database. Most approaches to association rule mining assume that the items within the dataset have a uniform distribution. Therefore, ...
Association rule mining using binary particle swarm optimization
In this paper, we developed a binary particle swarm optimization (BPSO) based association rule miner. Our BPSO based association rule miner generates the association rules from the transactional database by formulating a combinatorial global ...
Association rule mining through adaptive parameter control in particle swarm optimization
Association rule mining is a data mining task on a great deal of academic research has been done and many algorithms are proposed. Association rule mining is treated as a twofold process by most of the methods. It increases the complexity of the system ...
Comments