Abstract
In 2013, an efficient algorithm for mining class association rules, named CAR-Miner, has been proposed. It, however, still consumes much memory in storing Obidsets of itemsets and time in computing the intersection between two Obidsets. In this paper, we propose an improved algorithm for mining class association rules by using the difference between two Obidsets (d2O). Firstly, the d2O concept is developed. After that, a strategy for reducing the storage space and fast computing d2O is also derived. Experimental results show that the proposed algorithm is more efficient than CAR-Miner.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alcala-Fdez, J., Alcala, R., Herrera, F.: 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 (2011)
Coenen, F., Leng, P., Zhang, L.: The effect of threshold values on association rule based classification accuracy. Data & Knowledge Engineering 60(2), 345–360 (2007)
Hu, Y., Chen, R., Tzeng, G.: Finding fuzzy classification rules using data mining techniques. Pattern Recognition Letters 24(1-3), 509–519 (2003)
Li, W., Han, J., Pei, J.: CMAR: Accurate and efficient classification based on multiple class-association rules. In: The 1st IEEE International Conference on Data Mining, San Jose, California, USA, pp. 369–376 (2001)
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: The 4th International Conference on Knowledge Discovery and Data Mining, New York, USA, pp. 80–86 (1998)
Liu, B., Ma, Y., Wong, C.K.: Improving an association rule based classifier. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 504–509. Springer, Heidelberg (2000)
Nguyen, T.T.L., Vo, B., Hong, T.P., Thanh, H.C.: Classification based on association rules: A lattice-based approach. Expert Systems with Applications 39(13), 11357–11366 (2012)
Nguyen, T.T.L., Vo, B., Hong, T.P., Thanh, H.C.: CAR-Miner: An efficient algorithm for mining class-association rules. Expert Systems with Applications 40(6), 2305–2311 (2013)
Pach, F., Gyenesei, A., Abonyi, J.: Compact fuzzy association rule-based classifier. Expert Systems with Applications 34(4), 2406–2416 (2008)
Priss, U.: A classification of associative and formal concepts. In: The Chicago Linguistic Society’s 38th Annual Meeting, Chicago, USA, pp. 273–284 (2002)
Quinlan, J.R.: Introduction of decision tree. Machine Learning 1(1), 81–106 (1986)
Quinlan, J.R.: C4.5: program for machine learning. Morgan Kaufmann (1993)
Sun, Y., Wang, Y., Wong, A.K.C.: Boosting an associative classifier. IEEE Transactions on Knowledge and Data Engineering 18(7), 988–992 (2006)
Thabtah, F., Cowling, P., Hammoud, S.: Improving rule sorting, predictive accuracy and training time in associative classification. Expert Systems with Applications 31(2), 414–426 (2006)
Thabtah, F., Cowling, P., Peng, Y.: MMAC: A new multi-class, multi-label associative classification approach. In: The 4th IEEE International Conference on Data Mining, Brighton, UK, pp. 217–224 (2004)
Tolun, M.R., Abu-Soud, S.M.: ILA: An inductive learning algorithm for production rule discovery. Expert Systems with Applications 14(3), 361–370 (1998)
Tolun, M.R., Sever, H., Uludag, M., Abu-Soud, S.M.: ILA-2: An inductive learning algorithm for knowledge discovery. Cybernetics and Systems 30(7), 609–628 (1999)
Veloso, A., Meira Jr., W., Zaki, M.J.: Lazy associative classification. In: The 2006 IEEE International Conference on Data Mining (ICDM 2006), Hong Kong, China, pp. 645–654 (2006)
Veloso, A., Meira Jr., W., Gonçalves, M., Zaki, M.J.: Multi-label lazy associative classification. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 605–612. Springer, Heidelberg (2007)
Veloso, A., Meira Jr., W., Goncalves, M., Almeida, H.M., Zaki, M.J.: Calibrated lazy associative classification. Information Sciences 181(13), 2656–2670 (2011)
Vo, B., Le, B.: A novel classification algorithm based on association rules mining. In: Richards, D., Kang, B.-H. (eds.) PKAW 2008. LNCS (LNAI), vol. 5465, pp. 61–75. Springer, Heidelberg (2009)
Yang, G., Mabu, S., Shimada, K., Hirasawa, K.: An evolutionary approach to rank class association rules with feedback mechanism. Expert Systems with Applications 38(12), 15040–15048 (2011)
Yin, X., Han, J.: CPAR: Classification based on predictive association rules. In: SIAM International Conference on Data Mining (SDM 2003), San Francisco, CA, USA, pp. 331–335 (2003)
Zhang, X., Chen, G., Wei, Q.: Building a highly-compact and accurate associative classifier. Applied Intelligence 34(1), 74–86 (2011)
Zhao, S., Tsang, E.C.C., Chen, D., Wang, X.Z.: Building a rule-based classifier - A fuzzy-rough set approach. IEEE Transactions on Knowledge and Data Engineering 22(5), 624–638 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Nguyen, L.T.T. (2014). Mining Class Association Rules with the Difference of Obidsets. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8398. Springer, Cham. https://doi.org/10.1007/978-3-319-05458-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-05458-2_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05457-5
Online ISBN: 978-3-319-05458-2
eBook Packages: Computer ScienceComputer Science (R0)