Probabilistic Mining Model for Drugs Classification in Data Mining
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2015 by IJETT Journal|
|Year of Publication : 2015|
|Authors : Haritha Paidi, L. Prasanna Kumar
Haritha Paidi, L. Prasanna Kumar"Probabilistic Mining Model for Drugs Classification in Data Mining", International Journal of Engineering Trends and Technology (IJETT), V30(3),142-146 December 2015. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Nowaday’s finding chronic diseases and drugs are becoming more important for supporting the patient resource information. Extracting patient information from the text is most challenging and also critical. So for extracting patient information from these substantial bodies of texts we are using so many opinion mining techniques. In this paper we are extracting information from these substantial bodies of texts using one of the mining models of classification approach. The classification technique used is Naïve Bayesian classifier which is used for finding the causes that occur by using over doses of drugs and also to find the type of side effect that will occur. After completion of classification we are grouping the related drugs which are causing the same side effects by using Word Comparator Clustering algorithm. By implementing this application we can improve the efficiency and also provide more classification accuracy.
 M. Hu and B. Liu, “Mining and summarizing customer reviews,”in Proc. 10th ACM SIGKDD Int. Conf. KDD,Washington, DC, USA,
. B. Pang and L. Lee, “Opinion mining and sentiment analysis,” Found. Trends Inf. Ret., vol. 2, no. 1–2, pp. 1–135, Jan. 2008.
. L. Zhuang, F. Jing, and X. Zhu, “Movie review mining and summarization,” inProc. 15th ACM CIKM, New York, NY, USA, 2006,pp. 43–50
. A. Névéol and Z. Lu, “Automatic integration of drug indications from multiple health resources,” in Proc. 1st ACM Int. Health Inform. Symp., New York, NY, USA, 2010, pp. 666–673.
. J. Zrebiec and A. Jacobson, “What attracts patients with diabetes to an internet support group? A 21-month longitudinal website stuey,” Diabetic Med., vol. 18, no. 2, pp. 154–158, 2008.
 J.C. Benaloh. Secret sharing homomorphisms: Keeping shares of a secretsecret. In Crypto, pages 251–260, 1986.
 J. Brickell and V. Shmatikov.Privacy-preserving graph algorithms inthe semi-honest model. In ASIACRYPT, pages 236– 252, 2005.
 D.W.L. Cheung, J. Han, V.T.Y. Ng, A.W.C. Fu, and Y. Fu. A fastdistributed algorithm for mining association rules. In PDIS, pages 31–42, 1996.
 D.W.L Cheung, V.T.Y. Ng, A.W.C. Fu, and Y. Fu. Efficient miningof association rules in distibuted databases. IEEE Trans. Knowl. DataEng., 8(6):911–922,
. S. Lacoste-Julien, F. Sha, andM. Jordan, “DiscLDA: Discriminativelearning for dimensionality reduction and classification,” in Proc.Adv. NIPS, 2008, pp. 897–904.
. J. Demšar, “Statistical comparisons of classifiers over multipledata sets,” J. Mach. Learn. Res., vol. 7, pp. 1–30, Jan. 2006.
.R. Agrawal and R. Srikant, “Fast algorithms for mining association rules,” in Proc. 20th Int. Conf. VLDB, San Francisco, CA, USA,1994, pp. 487–499.
. A.-M. Popescu and O. Etzioni, “Extracting product features and opinions from reviews,” in Proc. Conf. Human Lang. Technol. Emp. Meth. NLP, Stroudsburg, PA, USA, 2005, pp. 339–346.
. B. Liu, M. Hu, and J. Cheng, “Opinion observer: Analyzing and comaring opinions on the web,” in Proc. 14th Int. Conf. WWW, New York, NY, USA, 2005, pp. 342–351.
. S. Baccianella, A. Esuli, and F. Sebastiani, “Multi-facet rating of product reviews,” in Proc. 31st ECIR , Berlin„ Germany, 2009, pp. 461–472.
. D. Blei, A. Ng, and M. Jordan, “Latent Dirichlet allocation,” J. Mach. Learn. Res., vol. 3, pp. 993–1022, Jan. 2003.
. Y. Jo and A. Oh, “Aspect and sentiment unification model for online review analysis,” in Proc. 4th ACM Int. Conf. WSDM, New York, NY, USA, 2011, pp. 815–824.
. D. Blei and J. Mcauliffe, “Supervised topic models,” in Proc. Adv. NIPS, 2007, pp. 121–128.
. D. Donoho and V. Stodden, “When does non-negative matrix factorization give a correct decomposition into parts?” in Proc. Adv. NIPS, 2003.
 H. Lee, J. Yoo, and S. Choi, “Semi-supervised nonnegative matrix factorization,” IEEE Signal Process. Lett., vol. 17, no. 1, pp. 4–7, Jan. 2010.
Data mining, classification,Naive Bayesianclassifier, Clusterization.