Data mining conciliations talented ways to expose secreted designs within huge volumes of data. These hidden designs can possibly be used to prediction forthcoming performance. The descriptive data mining tasks characterize the general properties of the data present in the database, while in contrast predictive data mining technique perform inference from the current data for making prediction. This overview briefly introduces these two most important techniques that perform data mining task as Predictive and Descriptive. Between this predictive and descriptive they consist of their own method as Classification, clustering, Data mining (knowledge discovery from data) may be viewed as the abstraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns and models from observed data or a method used for analytical process designed to explore data. We know Data mining as knowledge discovery. Basically, Extraction or “MINING” means knowledge from large amount of data. the prediction analysis technique provided by the data mining the future scenarios regarding to the current information can be predicted. The prediction analysis is the combination of clustering and classification. In order to provide prediction analysis there are several techniques presented through many researchers. In this paper describes various techniques proposed by various authors are analysed to understand latest trends in the prediction analysis.

Extraction, Predictive Techniques, Database, Classification, SVM, Clustering

[1] AbdelghaniBellaachia and ErhanGuven “Predicting Breast Cancer Survivability Using Data Mining Techniques”, Washington DC 20052, vol. 6, pp. 234-239,2010.
[2] Oyelade, O. J, Oladipupo, O. O and Obagbuwa, I. C “Application of k-Means Clustering algorithm for prediction of Students’ Academic Performance”, International Journal of Computer Science and Information Security, vol. 7, pp. 23-128,2010.
[3] AzharRauf, Mahfooz, Shah Khusro and HumaJaved“Enhanced K-Mean Clustering Algorithm to Reduce Number of Iterations and Time Complexity”, Middle-East Journal of Scientific Research, vol. 12, pp. 959-963,2012.
[4] Osamor VC, Adebiyi EF, Oyelade JO and Doumbia S “Reducing the Time Requirement of K-Means Algorithm” PLoS ONE, vol. 7, pp-56-62,2012
[5] AzharRauf, Sheeba, SaeedMahfooz, Shah Khusro and HumaJaved (2012), “Enhanced K-Mean Clustering Algorithm to Reduce Number of Iterations and Time Complexity,” Middle-East Journal of Scientific Research, vol. 5, pp. 959-9632012.
[6] Thair Nu Phyu, “Survey of Classification Techniques in Data Mining”, Proceedings of the International Multi Conference of Engineers and Computer Scientists, volume 3, issue 12, pp551-559, IMECS,2009.
[7] Chuan-Yu Chang, Chuan-Wang Chang, Yu-Meng Lin, Application of Support Vector Machine for Emotion Classification”, 2012 Sixth International Conference on Genetic and Evolutionary Computing, volume 12, issue 5, pp- 103- 111 ,2012.
[8] Himani Bhavsar, Mahesh H. Panchal, “A Review on Support Vector Machine for Data Classification”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 1, Issue 10,2012.
[9] Min Chen, YixueHao, Kai Hwang, Fellow, IEEE, Lu Wang, and Lin Wang “Disease Prediction by Machine Learning over Big Data from Healthcare Communities”, IEEE, vol. 15, pp- 215-227 ,2017.
[10] Akhilesh Kumar Yadav, DivyaTomar and SonaliAgarwal Clustering of Lung Cancer Data Using Foggy KMeans”, International Conference on Recent Trends in Information Technology (ICRTIT), vol. 21, pp.121-126.,2014.
[11] [Sanay Chakrabotry, Prof. N.K Nigwani and Lop Dey “Weather Forecasting using Incremental K-means Clustering”, vol. 8, pp. 142-147.2014.
[12] Chew Li Sa., Bt Abang Ibrahim, D.H., Dahliana Hossain, E. and bin Hossin, M. "Student performance analysis system (SPAS)", in Information and Communication Technology for The Muslim World (ICT4M),The 5th International Conference on, vol.15, pp.1-6.,2014.
[13] Qasem A. Al-Radaideh, Adel Abu Assaf and EmanAlnagi Predicting Stock Prices Using Data Mining Techniques”, the International Arab Conference on Information Technology (ACIT’2013), vol. 23, pp. 32-38, (2013),
[14] K. Rajalakshmi, Dr. S. S. Dhenakaran and N. Roobin “Comparative Analysis of K-Means Algorithm in Disease Prediction”, International Journal of Science, Engineering and Technology Research (IJSETR), Vol. 4, 2015.
[15] BalaSundar V, T Devi and N Saravan, “Development f a Data Clustering Algorithm for Predicting Heart”, International Journal of Computer Applications, vol. 48, pp. 423-428,2012.
[16] DaljitKaur and KiranJyot “Enhancement in the Performance of K-means Algorithm”, International Journal of Computer Science and Communication Engineering, vol. 2 pp. 724-729,2013.
[17] Ming, J., Zhang, L., Sun, J.& Zhang, Y, “Analysis models of technical and economic data of mining enterprises based on big data analysis”, International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 2018, IEEE, 3rHimani Rani et al, International Journal of Computer Science and Mobile Computing, Vol.8 Issue., pg. 15-22, 5 may 2019.
[18] Sakhare, A. V., & Kasbe, P. S “A review on road accident data analysis using data mining techniques”, International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2017
[19] Chauhan, C., & Sehgal, S, “A review: Crime analysis using data mining techniques and algorithms”, International Conference on Computing, Communication and Automation (ICCCA), 2017
[20] Anoopkumar M, & Rahman, A. M. J. M. Z, “A Review on Data Mining techniques and factors used in Educational Data Mining to predict student amelioration, International Conference on Data Mining and Advanced Computing.
[21] Lee, E., Jang, Y., Yoon, D.-M., Jeon, J., Yang, S., Lee, S, “Kim, K.-JGame Data Mining Competition on Churn Prediction and Survival Analysis” using Commercial Game Log Data Transactions on Games, IEEE, 2018.
[22] Mortenson, M. J., Doherty, N. F., & Robinson, S. (Operational research from Taylorism to tera bytes:a research agenda for the analytics age. European Journal of OperationalResearch, 583-595,2014
[23] Delen, D., &Demirkan, H. Data, information and analytics as services. Decision Support Systems, 359- 363.2013.
[24] .Lechevalier, D., Narayanan, A., &Rachuri, S. Towards a DomainSpecific Framework for PredictiveAnalytics in Manufacturing. IEEE International Conference on Big Data Gaithersburg: National Institute of Standards and Technology, (pp. 987-995),2014.