A review on comparative performance analysis of associative classifiers

Ali, et al.

International Journal of Advanced and Applied Sciences

Int. j. adv. appl. sci.

EISSN: 2313-3724

Print ISSN: 2313-626X

Volume 4, Issue 6 (June 2017), Pages: 96-103

Title: A review on comparative performance analysis of associative classifiers

Author(s): Zulfiqar Ali ^{1, 2,} * , Waseem Shahzad ¹, Syed Khuram Shahzad ²

Affiliation(s):

¹Department of Computer Science, National University of Computer & Emerging Science, Islamabad, Pakistan
²Department of Computer Science and Information Technology, The University of Lahore, Lahore, Pakistan

https://doi.org/10.21833/ijaas.2017.06.014

Full Text - PDF XML

Abstract:

In this study we provided comparative study of associative classifiers which can be exploited for the discovery of business rules from the huge structured and unstructured data that can be used in the business analytic. Associative classification is a hybrid approach combining the classification rules mining and association rules mining that are two important data mining tasks. There are various emerging classification problems in various domains of knowledge like medical data, images, audio, video and textual data. Associative Classification approaches are exploited in various fields for the classification purposes. We compare the selective associative classification methods namely CBA, CBA2, CMAR-C, CFAR-C, CPAR-C, and Fuzzy-FARCHD-C by exploiting the implementation of these methods in KEEL data mining tool on public datasets. Our experimental results reveals that the performance of the Fuzzy-FARCHD-C is promising than other methods in terms of accuracy. The performance of the associative classifiers drastically decreases on the datasets with higher number classes and attributes.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keywords: Associative classification, Association rule mining, KEEL, Data mining

Article History: Received 26 December 2016, Received in revised form 10 March 2017, Accepted 15 April 2017

Digital Object Identifier:

https://doi.org/10.21833/ijaas.2017.06.014

Citation:

Ali Z, Shahzad W, and Shahzad SK (2017). A review on comparative performance analysis of associative classifiers. International Journal of Advanced and Applied Sciences, 4(6): 96-103

http://www.science-gate.com/IJAAS/V4I6/Ali.html

References:

Agrawal R and Srikant R (1994). Fast algorithms for mining association rules. In the 20th International Conference on Very Large Data Bases, VLDB, 1215: 487-499.

Alcalá J, Fernández A, Luengo J, Derrac J, García S, Sánchez L, and Herrera F (2010). Keel data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework. Journal of Multiple-Valued Logic and Soft Computing, 17(2-3): 255-287.

Alcala-Fdez J, Alcala R, and Herrera F (2011). A fuzzy association rule-based classification model for high-dimensional problems with genetic rule selection and lateral tuning. IEEE Transactions on Fuzzy Systems, 19(5): 857-872. https://doi.org/10.1109/TFUZZ.2011.2147794

Baig AR and Shahzad W (2012). A correlation-based ant miner for classification rule discovery. Neural Computing and Applications, 21(2): 219-235. https://doi.org/10.1007/s00521-010-0490-5

Chen Z and Chen G (2008). Building an associative classifier based on fuzzy association rules. International Journal of Computational Intelligence Systems, 1(3): 262-273. https://doi.org/10.1080/18756891.2008.9727623

Jin P, Zhu Y, Hu K, and Li S (2006). Classification rule mining based on ant colony optimization algorithm. In: Huang D, Li K, Irwin GW (Eds.), Intelligent Control and Automation: 654-663. Springer, Kunming, China. https://doi.org/10.1007/978-3-540-37256-1_82

Li W, Han J, and Pei J (2001). CMAR: Accurate and efficient classification based on multiple class-association rules. In the IEEE International Conference on Data Mining (ICDM 2001), IEEE, San Jose, USA: 369-376. https://doi.org/10.1109/ICDM.2001.989541

Liu B, Abbass HA, and McKay B (2002). Density-based heuristic for rule discovery with ant-miner. In The 6th Australia-Japan Joint Workshop On Intelligent And Evolutionary System, School Of Computer Science, Australian Defence Force Academy, University of New South Wales, Australia.

Liu B, Abbass HA, and McKay B (2003). Classification rule discovery with ant colony optimization. In the IEEE/WIC International Conference on Intelligent Agent Technology, IEEE, Halifax Regional Municipality, Canada. https://doi.org/10.1109/IAT.2003.1241052

Liu B, Ma Y, and Wong CK (2001). Classification using association rules: weaknesses and enhancements. In: Grossman R, Namburu R, Kamath C, Kegelmeye P (Eds.), Data Mining for Scientific and Engineering Applications: 591-605. Springer Science and Business Media, Berlin, Germany. https://doi.org/10.1007/978-1-4615-1733-7_30

Ma BLWHY and Liu B (1998). Integrating classification and association rule mining. In the 4th International Conference on Knowledge Discovery and Data Mining, AAAI Press: 80-86.

Martens D, De Backer M, Haesen R, Vanthienen J, Snoeck M, and Baesens B (2007). Classification with ant colony optimization. IEEE Transactions on Evolutionary Computation, 11(5): 651-665. https://doi.org/10.1109/TEVC.2006.890229

Otero FE, Freitas AA, and Johnson CG (2008). cAnt-Miner: an ant colony classification algorithm to cope with continuous attributes. In the International Conference on Ant Colony Optimization and Swarm Intelligence, Springer Berlin Heidelberg, Brussels, Belgium: 48-59. https://doi.org/10.1007/978-3-540-87527-7_5

Parpinelli RS, Lopes HS, and Freitas A (2002). Data mining with an ant colony optimization algorithm. IEEE Transactions on Evolutionary Computation, 6(4): 321-332. https://doi.org/10.1109/TEVC.2002.802452

Salzberg SL (1994). C4. 5: Programs for machine learning by j. ross quinlan. Morgan Kaufmann Publishers, inc., 1993. Machine Learning, 16(3): 235-240. https://doi.org/10.1007/BF00993309

Shahzad W and Baig A (2011). Hybrid associative classification algorithm using ant colony optimization. International Journal of Innovative Computing, Information and Control, 7(12): 6815-6826.

Shahzad W and Baig AR (2010). Compatibility as a heuristic for construction of rules by artificial ants. Journal of Circuits, Systems, and Computers, 19(01): 297-306. https://doi.org/10.1142/S0218126610006244

Soni S and Vyas O (2010). Using associative classifiers for predictive analysis in health care data mining. International Journal of Computer Applications, 4(5): 33-37. https://doi.org/10.5120/821-1163

Thabtah F (2007). A review of associative classification mining. The Knowledge Engineering Review, 22(01): 37-65. https://doi.org/10.1017/S0269888907001026

Vyas R, Sharma LK, Vyas OP, and Scheider S (2008). Associative classifiers for predictive analytics: Comparative performance study. In the 2nd UKSIM European Conference on Computer Modeling and Simulation, IEEE: 289-294. Liverpool, UK. https://doi.org/10.1109/ems.2008.29

Yin and Han (2005). Efficient classification from multiple heterogeneous databases. In the Ninth European Conference on Principles and Practice of Knowledge Discovery in Databases. pp. 404–416. https://doi.org/10.1007/11564126_40