MMCAR: Modified multi-class classification based on association rule

Classification using association is a recent data mining approach that integrates association rule discovery and classification. A modified version of the Multi-class Classification based on Association Rule (MCAR) is proposed in this paper. The proposed classifier, known as Modified Multi-class Classification based on Association Rule, MMCAR, employs a new rule production function which resulted only relevant rules are used for prediction. Experiments on UCI data sets using different classification learning algorithms (C4.5, RIPPER, MCAR) is performed in order to evaluate the effectiveness of MMCAR. Results show that the MMCAR produced higher accuracy compared to C4.5 and RIPPER. In addition, the average number of rules generated by MMCAR is less than the one produced by MCAR.

[1]  William W. Cohen.  Fast Effective Rule Induction , 1995, ICML.

[2]  Jian Pei,et al.  CMAR: accurate and efficient classification based on multiple class-association rules , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[3]  Jiawei Han,et al.  CPAR: Classification based on Predictive Association Rules , 2003, SDM.

[4]  Elena Baralis,et al.  A Lazy Approach to Associative Classification , 2008, IEEE Transactions on Knowledge and Data Engineering.

[5]  Ke Wang,et al.  Mining confident rules without support requirement , 2001, CIKM '01.

[6]  Elena Baralis,et al.  On support thresholds in associative classification , 2004, SAC '04.

[7]  Peter I. Cowling,et al.  Improving rule sorting, predictive accuracy and training time in associative classification , 2006, Expert Syst. Appl..

[8]  J. R. Quinlan,et al.  Data Mining Tools See5 and C5.0 , 2004 .

[9]  Zhonghua Tang,et al.  A New Class Based Associative Classification Algorithm , 2007, IMECS.

[10]  Henry Linger,et al.  Journal of Information and Knowledge Management , 2003 .

[11]  T. L. McCluskey,et al.  A New Classification Based on Association Algorithm , 2010, J. Inf. Knowl. Manag..

[12]  D. J. Newman,et al.  UCI Repository of Machine Learning Database , 1998 .

[13]  Gary Geunbae Lee,et al.  Text Categorization Based on Boosting Association Rules , 2008, 2008 IEEE International Conference on Semantic Computing.

[14]  Mohammed J. Zaki,et al.  Fast vertical mining using diffsets , 2003, KDD '03.

[15]  Peter I. Cowling,et al.  A greedy classification algorithm based on association rule , 2007, Appl. Soft Comput..

[16]  Peter I. Cowling,et al.  MCAR: multi-class classification based on association rule , 2005, The 3rd ACS/IEEE International Conference onComputer Systems and Applications, 2005..

[17]  Wei-Yin Loh,et al.  A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms , 2000, Machine Learning.

[18]  Dimitris Meretakis,et al.  Extending naïve Bayes classifiers using long itemsets , 1999, KDD '99.

[19]  Wynne Hsu,et al.  Integrating Classification and Association Rule Mining , 1998, KDD.