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A Comprehensive Analysis on Associative Classification in Medical Datasets


  • Department of Computer Science, Bharathiyar University, Coimbatore - 641046, Tamil Nadu, India
  • Department of Computer Applications, Queen Mary’s College, Chennai - 600004, Tamil Nadu, India


Association rule mining along with classification technique is capable of finding informative patterns from large data sets. Output of this technique is of the form if-then which is easy to understand for the end users and also for prediction. Termed as Associative Classification, it is having wide application on medical domain in diagnosing diseases and to analyze medical datasets which are unstructured, heterogeneous, incomprehensible and voluminous. Analysing these data allows physicians to predict the diseases as well as to take vital decisions. This paper presents a detailed study on associative classification and the phases of associative classification procedure. Several associative classification methods viz., CPAR, MMAC, CAR, CBA, CMAR, etc. along with their merits and demerits are also presented in a lucid manner.


Association, Associative Classification, Classification, Knowledge Discovery, Medical Datasets

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  • Kononenko, Igor, Kukar M. Machine learning and data mining: Introduction to principles and algorithms. Horwood Publishing; 2007.
  • Chen MC, Chiu AL, Chang HH. Mining changes in customer behavior in retail marketing. Expert Systems with Applications. 2005; 28(4):773–81.
  • Thabtah F, Cowling P, Peng Y. MCAR: Multi-Class Classification based on Association Rule. The 3rd ACS/IEEE International Conference on Computer Systems and Applications;2005.
  • Rygielski C, Wang JC, Yen DC. Data mining techniques for customer relationship management. Technology in Society.2002 Nov; 24(4):483–502.
  • Weiyang L, Alvarez SA, Ruiz C. Efficient adaptive-support association rule mining for recommender systems. Data Mining and Knowledge Discovery. 2002; 6(1):83–105.
  • Yin X, Han J. CPAR: Classification based on Predictive Association Rules. SDM. 2003; 3:331–5.
  • Li W, Han J, Pei J. CMAR: Accurate and efficient classification based on multiple class-association rules. Proceedings IEEE International Conference on Data Mining-ICDM;San Jose, CA. 2001. p. 369–76.
  • Tao F, Murtagh F, Farid M. Weighted association rule mining using weighted support and significance framework. Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,ACM; 2003. p. 661–6.
  • Kirkos E, Spathis C, Manolopoulos Y. Data mining techniques for the detection of fraudulent financial statements.Expert Systems with Applications. 2007; 32(4):995–1003.
  • Zheng Y, Liu P, Lei L, Yin J. R-C4. 5 decision tree model and its applications to health care dataset. 2005 Proceedings of International Conference on Services Systems and Services Management (ICSSSM’05); 2005. p. 1099–103.
  • Baralis E, Chiusano S, Garza P. A lazy approach to associative classification. IEEE Transactions on Knowledge and Data Engineering. 2008; 20(2):156–71.
  • Yu G, Li K, Shao S. Mining high utility itemsets in large high dimensional data. Proceedings of the 1st International Conference on Forensic Applications and Techniques in Telecommunications, Information, and Multimedia and Workshop. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering);2008. p. 47.
  • Lin W, Alvarez SA, Ruiz C. Efficient adaptive-support association rule mining for recommender systems. Data Mining and Knowledge Discovery. 2008; 6(1):83–105.
  • Thabtah F. A review of associative classification mining.The Knowledge Engineering Review. 2007; 22(01):37–65.
  • Zaki MJ. Mining non-redundant association rules. Data-Mining and Knowledge Discovery. 2004; 9(3):223–48.
  • Kotsiantis SB, Zaharakis I, Pintelas P. Supervised machine learning: A review of classification techniques. Emerging Artificial Intelligence Applications in Computer Engineering;2007. p. 3–24.
  • Nauck D, Kruse R. Obtaining interpretable fuzzy classification rules from medical data. Artificial Intelligence in Medicine.1999; 16(2):149–69.
  • Scheffer T. Finding association rules that trade support optimally against confidence. Principles of Data Mining and Knowledge Discovery. Heidelberg: Springer Berlin; 2001. p.424–35.
  • Rak R, Kurgan L, Reformat M. Multi-label associative classification of medical documents from medline. IEEE Proceedings Fourth International Conference on Machine Learning and Applications; 2005. p. 177–86.
  • Jabbar MA, Deekshatulu BL, Chandra P. Heart disease prediction system using associative classification and genetic algorithm. International Conference on Emerging Trends in Electrical, Electronics and Communication Technologies; 2013. p. 183–92.
  • Karegowda AG, Manjunath AS, Jayaram MA. Application of genetic algorithm optimized neural network connection weights for medical diagnosis of pima Indians. International Journal on Soft Computing. 2001; 2(2):17–21.
  • Elveren E, Yumusak N. Tuberculosis disease diagnosis using artificial neural network trained with genetic algorithm.Journal of Medical Systems. 2011; 35(3):329–32.
  • Tomar PPS, Singh R. Evolutionary continuous genetic algorithm for clinical decision support system. African Journal of Computing and ICT. 2013; 6(1):127–46.
  • Su Z, Song W, Cao D, Li J. Discovering informative association rules for associative classification. IEEE International Symposium on Knowledge Acquisition and Modeling Workshop; Wuhan. 2008. p. 1060–3.
  • Shekhawat PB, Dhande SS. A classification technique using associative classification. International Journal of Computer Application. 2011; 20(5):20–8.
  • Ganganwar V. An overview of classification algorithms for imbalanced datasets. International Journal of Emerging Technology and Advanced Engineering. 2012; 2(4):42–7.
  • Jiang Y, Shang J, Liu Y. Maximizing customer satisfaction through an online recommendation system: A novel associativeassociative classification model. Decision Support Systems.2010; 48(3):470–9.
  • Ye Y, Li T, Jiang Q, Wang Y. CIMDS: Adapting postprocessing techniques of associative classification for malware detection. IEEE Transactions on Systems, Man, and Cybernetics,Part C: Applications and Reviews. 2010; 40(3):298–307.
  • Dorbala S, DrNayak R. Integrated data mining approach for security alert system. International Journal of Research Computer Communication Technology. 2014; 3(4):464–8.
  • Phua C, Lee V, Smith K, Gayler R. A comprehensive survey of data mining-based fraud detection research. Artificial Intelligence Computer Science. 2010; 14.
  • Sangsuriyun S, Marukatat S, Waiyamai K. Hierarchical Multi-label Associative Classification (HMAC) using negative rules. 9th IEEE International Conference on Cognitive Informatics (ICCI); Beijing. 2010. p. 919–24.
  • Li X, Qin D, Yu C. ACCF: Associative Classification based on Closed Frequent itemsets. Fifth International Conference on Fuzzy Systems and Knowledge Discovery FSKD’08;Shandong. 2008. p. 380–4.
  • Chen G, Liu H, Yu L, Wei Q, Zhang X. A new approach to classification based on association rule mining. Decision Support Systems. 2008; 42(2):674–89.
  • Kundu G. Islam MM. Munir S. Bari MF. ACN: An associative classifier with negative rules. 11th IEEE International Conference on Computational Science and Engineering,CSE’08; Sao Paulo. 2008. p. 369–75.


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