Data mining in the stream data handles quality and data analysis using extremely large and infinite amount of data and disk or memory with limited volume[2]. In such traditional transaction environment it is impossible to perform frequent items mining because it requires analyzing which item is a frequent one to continuously incoming stream data and which is probable to become a frequent item. This paper analyze a way to predict frequent items using linear regression model[5] to the continuously incoming one dimensional stream data like the time series data. By establishing the regression model from the stream data, it may be used as a prediction model to uncertain items. The proposing way will exhibit its effectiveness through experiment in stream data.

Data mining, Time Series Data, Regression Techniques, Stream Data

[1] D.F. Andrews, :A robust method for multiple linear regression,Technometrics , vol 16, 1974, pp 125 - 127.
[2]Chai, Eun Hee Kim and Long Jin:prediction of Frequent Items to OneDimensional Stream Data; Fifth International Conference on Computational Science and Applications ; page 353-360, 2001
[3]Y. Chen, G.Dong, J.Han, B.W.Wah, and J.Wang : .Multi-Dimensional Regression Analysis of Time- Series Data Streams; Proc. Int. Conf. Very Large Data Bases;Hong Kong, China, Aug. 2002.
[4]C. Giannella, J. Han, J. Pei, X. Yan, and P. S. Yu, :Mining Frequent Patterns in Data Streams at Multiple Time Granularities, In H. Kargupta, A. Joshi, K. Sivakumar, and Y.Yeshar(eds.), Next Generation Data Mining, AAAI/MIT, 2003.
[5]R. Hayward; A Basic Approach to Linear Regression; RWJ linical Scholars Program; pp1-3,University of Michigan , 2005.
[6]O.B.Yaik, C.H.Yong, and FHaron, Time Series Prediction using Adaptive Association rules,InProc.of DFMA05, pp.310-314, 2005.
[7]Omid Rouhani-Kalleh; Algorithms for Fast Large Scale data Mining Using Logistic Regression; Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining; pp 155-162, 2007.
[8]Feng Zhao, Qing-Hua A Li :A Plane Regression Based Sequence Forecast Algorithms for Stream Data ; Proc. of the Fourth International Conference on Machine Learning and Cybernetics; pp-1559-1562 Guangzhou,18-21 August, 2005.
[9]Y. Peng, G. Kou, Y. Shi, Z. Chen; A Descriptive Framework for the Field of Data Mining and Knowledge Discovery. International Journal of Information Technology and Decision Making, Volume 7, Issue 4: 639 – 682; 2000
[10] Perlich, C,Provost, F., Simonoff, J. S. Tree Induction verses. Logistic Regression:A Learning-Curve Analysis. Journal of Machine Learning Research Vol. 4 pp-211- 255. 2003.
[11]Amir Bar-Or, Daniel Keren, Assaf Schuster, and Ran Wolff: Hierarchical Decision Tree Induction in istributed Genomic Databases; IEEERANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,VOL. 17;pp; 1138- 1150,2007.
[12]Qi Luo; Advancing Knowledge Discovery and Data Mining; Workshop on Knowledge Discovery and Data Mining pp;3-5, 2008.
[13]Fayyad, Usama; Gregory Piatetsky-Shapiro, and adhraic Smyth; From Data Mining to Knowledge Discovery in Databases. -pp:12-17, June 2008.