International Journal of Engineering Research in Electronics and Communication Engineering

Survey of Associative Classification Techniques for Text Mining

Author : Asst. Prof. Ms. Shweta M. Kambare ¹ Asst. Prof. Mrs. Varsha A. Jujare ² Asst. Prof. Mr. Atul A. Kumbhar ³

Date of Publication :15th February 2017

Abstract: Amount of unorganized text data is increasing day by day as the use of internet is increasing. Proper classification and knowledge discovery from these documents is an important area for research. Approximately 80% of the information of an organization is stored in unstructured textual format, in the form of reports, email, views and news etc. So there is need of automatic retrieval of useful knowledge from the huge amount of textual data in order to assist the human analysis. Associative classification is one of the most efficient techniques for text classification. Associative classification is integration of association rule mining and classification rule mining. In this paper, different techniques of associative classification are discussed in brief

Reference :

1. Liu, B., Hsu, W., Ma, Y. “Integrating classification and association rule mining” Proceedings of the KDD, (pp. 80-86). New York, NY. (1998)
2. Wenmin Li Jiawei Han Jian Pei” CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules” Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference
3. Yin, X., Han, J “CPAR: Classification based on predictive association rule” Proceedings of the SDM p. 369-376. San Francisco, CA. (2003).
4. Thabtah, F., Cowling, P., Peng, Y “MMAC: A new multi-class, multi-label associative classification approach” Proceedings of the Fourth IEEE (2004). International Conference on Data Mining (ICDM ’04), (pp. 217-224). Brighton, UK. (Nominated for the Best paper award).
5. Rafal Rak, Wojciech Stach, Osmar R. Za¨Ä±ane, and Maria-Luiza Antonie “Considering Re-occurring Features in Associative Classifiers” Springer-Verlag Berlin Heidelberg(2005).
6. Thabtah, F., Cowling, P., Peng, Y “MCAR: Multi-class classification based on association rule approach” Proceeding of the 3rd IEEE International Conference on Computer Systems and Applications p. 1-7. Cairo, Egypt. (2005).