Open Access Open Access  Restricted Access Subscription or Fee Access

A Survey on Data Set Based Prediction Techniques of Data Mining

Priyanka Pitale, Minu Choudhary

Abstract


Prediction is an ultimate application of data mining. The term Predictive Data Mining is usually applied to data mining tasks that are used to predict some response of interest like disease prediction, weather prediction, sales prediction etc. Predicting the future trends and helps companies to take sound decisions, based on knowledge and information. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. An algorithm or program that tries to predict the best probability of an outcome is said to be a predictive model. A predictive model uses different data mining techniques based on different tasks and past data available.

 In this survey paper we will survey some data mining techniques that can be used by prediction models for prediction in various situation.

 


Keywords


Prediction, Data Mining, Predictive Models, Disease Prediction, Weather Prediction, Sales Prediction.

Full Text:

PDF

References


Takashi Kimoto, Kazuo Asakawa ,Morio Yoda and Masakazu Takeoka , “Stock Market Prediction System with Modular Neural Networks ”,1990 IJCNN International Joint Conference on Neural Networks, Vol-1, San Diego, CA , USA pp. 1-6.

Sayan Mukherjee ,Edgar Osuna,Federico Girosi, “Nonlinear Prediction of Chaotic Time Series Using Support Vector Machine ” , Proceeding of IEEE NNSP'97, Amelia Island, FL, 24-26 Sep., 1997

Jaehyun Sim , Seung-Yeon Kim and Julian Lee, “Prediction of protein solvent accessibility using fuzzy k -nearest neighbor method ”, Oxford Journals ,Vol. 21 no. 12 2005, pages 2844–2849 .

Kari Laasonen, “Prediction of Mobile User Routes from Cellular Data”, 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, October 3-7, 2005. Proceedings, Springer Berlin / Heidelberg, Monday, November 07, 2005 ,pp-569-576.

Steven Rovnyak , Stein Kretsinger , James Thorp , Donald Brown , “Decision Trees For Real-Time Transient Stability Prediction ” , IEEE Transactions on Power Systems, Vol. 9, No. 3. August 1994 , pp-1417-1426.

Kiryung Lee, Dong Sik Kim, Taejeong Kim, “Regression-based prediction for blocking artifact reduction in JPEG-compressed images ”, IEEE Transactions on Image Processing, Electron. & Telecommun. Res. Inst., Daejeon, South Korea,14 Issue: 1, pp-36 - 48 .

Hongyu Sun, Henry X Liu, Heng Xiao, Bin Ran, “Short Term Traffic Forecasting Using the Local Linear Regression Model”, Center for Traffic Simulation Studies, Institute of Transportation Studies, UC Irvine, 07-01-2002.

C C Toner, C J Broomhead, I H Littlejohn,G S Samra “Prediction of postoperative nausea and vomiting using a logistic regression model”, British Journal of Anesthesia. 1996, pp-347-351.

Panagiotis Sentas, Lefteris Angelis, “Categorical missing data imputation for software cost estimation by multinomial logistic regression” , Journal of Systems and Software, Volume 79, Issue 3, March 2006, Pages 404-414.

Xiaobo Zhou, Xiaodong Wang, Edward R. Dougherty, “Gene prediction using multinomial probit regression with Bayesian gene selection”, EURASIP Journal on Applied Signal Processing, Volume 2004, 1 January 2004, pp- 115-124.

F.J. Nogales, J Contreras, A.J Conejo, R. Espinola, “Forecasting next-day electricity prices by time series models”, IEEE Transactions on Power Systems, Volume 17Issue:2, pp- 342 – 348.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.