In clustering, objects are clustered or grouped based on the principle of maximizing the intra-class similarity and minimizing the inter-class similarity. This paper proposes use of BIRCH hierarchical clustering method on large amount of numerical data by integration of hierarchical clustering and other clustering methods such as iterative partitioning methods such as k-means and k-medoids and their comparison. Clustering feature and clustering feature tree (CF tree) will be used to summarize cluster representations. With this clustering method we can achieve good speed and scalability.

1. Han J. and Kamber M. : "Data Mining: Concepts and Techniques," Morgan Kaufmann Publishers, San Francisco, 2000.
2. Xiaozhe Wang, Kate Smith and Rob Hyndman: " Characteristic-Based Clustering for Time Series Data", Data Mining and Knowledge Discovery , Springer Science + Business Media, LLC. Manufactured in the United States, 335–364, 2006.
3. Li Wei, Nitin Kumar,Venkata Lolla and Helga Van Herle:"A practical tool for visualizing and data mining medical time series", Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05) 106-125, 2005.
4. Eamonn Keogh, Selina Chu,David Hart and Michael Pazzani:" An online algorithm for segmenting time series", 0-7695-1 119-8/01 IEEE, 2001.
5. Xiao-Tao Zhang, Wei Zhang and Xiong Xiong:" A model based clustering for time-series with irregular interval", Proceedings of the Third International Conference on Machine Learhg and Cybernetics, Shanghai, 26-29, August 2004.
6. Hui Ding, Goce Trajcevski and Eamonn Keogh:" Querying and mining of time series data:Experimental comparison of representations and distance measures" , PVLDB '08, August 23-28, 2008, Auckland, New Zealand, 2008.
7. Ehsan Hajizadeh, Hamed Davari Ardakani and Jamal Shahrabi:"Appilication of data mining techniques in stock market", Journal of Economics and International Finance Vol. 2(7), pp. 109-118, July 2010.
8. Jiangjiao Duan, WeiWang , Bing Liu and Baile Shi:" Incorporating with recursive model training in time series clustering", Proceedings of the 2005 The Fifth International Conference on Computer and Information Technology (CIT'05), IEEE2005.
9. Fatih Altiparmak, Hakan Ferhatosmanoglu, Selnur Erdal, and Donald C. TrostFaith Altipar: " Information Mining Over Heterogeneous and High-Dimensional Time- Series Data in Clinical Trials Databases", IEEE Transactions On Information Technology In BioMedicine, VOL. 10, 215-239, APRIL 2006.
10. Jinfei Xie, Wei-Yong Yan:" A Qualitative Feature Extraction Method for Time Series Analysis", Proceedings of the 25th Chinese Control Conference 7–11 August, 2006, Harbin, Heilongjiang, 2006.
11. Xiaoming lin, Yuchang Lu, Chunyi Shi:" Cluster Time Series Based on Partial Information", IEEE SMC TPUl, p. no. 254-262, year 2002.

Mining Frequent Patterns Association Correlation Classification And Prediction Cluster Analysis Outlier Analysis And Evolution Analysis