Multi Relational Data Mining Approaches in the Panchayat Raj Department in Orissa Govt
Joe Mckendrick, “Reversing the Supply Chain”, Teradata Magazine - Applied Solutions, Vol.3No.3,2003.
Jaiwei Han and Micheline Kamber, Data Mining: Concepts and Techniques, Morgan Kaufman Publishers, San Francisco, 2001.
Steven Salzberg and Arthur L. Delcher, “Best-Case ResultsforNearest-NeighborLearning”,IEEE transactions on Pattern Analysis Machine Intelligence, Vol. 17, No. 6, 1995.
Networks Jorma Laaksonen and Erkki Oja, “Classification with learning k-Nearest Neighbors”, International Conference on Neural, 1996.
David Hand, Heikki Mannila, and Padhraic Smyth, principle of Data Mining, MIT Press, Cambridge, 2001
Ian Witten and Eibe Frank, Data MinPractical Machine Learning Tools and Techniques,Morgan aufman Publishers, San Francisco, 1999
Neelamadhab& Rasmita “Data warehousing and OLAP,MRDM technology In the decision support system in the21st century”
Zurada, J., and Lonial, S., 2005, "Comparison of the Performance of Several Data Mining Methods for Bad Debt Recovery in the Healthcare Industry." the Journal of Applied Business Research, 21(2), 37-53
Deng, B., Liu, X., “Data Mining in Quality Im provement”. Proceedings of the Twenty-Seventh Annual SAS Users Group International Conference 2002 by SAS Institute Inc., Cary, NC, USA. ISBN 1-59047-061-3.
R.Agrawal and R.Srikant.Fast Algorithm for mining association rules .In Jorge.B.Bocca,Matthias Jarke and Carlo Zanniolo,editiofs ,Proc.20th int.Conf.Very Large Data Base, and ,pages 487-499 ,morgan Kaufmann,12-15
K.J.Cios and G.W,Moore Uniqueness of Medical data mining .Artificial Intelligence in Medicine ,26:1-24,2002
V.Crestenia-Jensen and N.Ssoparkar.Frequent item sets counting across multiple tables .In 4th Pacific-Asian conference on Knowledge Discovery and Data Mining(PAKDD :2000),pages 49-61
L.DeReadt.Data mining in Multi relational data bases.4th Europian,Conference on Principles and Practice of Knowledge
L.Dehaspe and H.Toivonen .Discovery of frequent DATALOG patterns. Data Mining and knowledge Discovery,3(1).:7-36
Armstrong et al.08] Armstrong, J.S., Green, K.C., Soon, W.:“Polar Bear Population Forecasts: A Public-policy Forecasting Audit”; Interfaces, 38 (2008), 382-405.
Džeroski, A. Kobler, V. Gjorgijoski, P. Panov Using Decision Trees to Predict Forest Stand Heght and Canopy Cover from LANDSAT and LIDAR data. 20th Int. Conf. on Informatics for Environmental Protection - Managing Environmental Knowledge - ENVIROINFO 2006.
“WEKA 3: Data Mining Software in Java” (n.d.) Retrieved March 2007 from http://www.cs.waikato.ac.nz/ml/weka/`
Jain A.K, Murty M.N., Flynn P.J., (1999) “Data Clustering: review” ACM Computing Surveys, 31, 3:264-323.
Official website of Government of India, Ministry of agriculture. http://agricoop.nic.in.
Pham, D.T. and Afify, A.A. (2006) “Clustering techniques and their applications in engineering”. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical EngineerinScience
A.K. Jain, M.N. Murty, and P. J. Flynn,(1999) “Data clustering: a review”, ACM Computing Surveys (CSUR), Vol.31, Issue 3,, 1999.
Nargess Memarsadeghi , Dianne P. O'Leary,(2003) “Classified Information: The Data Clustering Problem”, Computing in Science and Engineering, v.5 n.5, September p.54-60,
Yifan Li , Jiawei Han , Jiong Yang, (2004) “Clustering moving objects”, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, Seattle, WA, USA
Sherin M. Youssef , Mohamed Rizk , Mohamed El-Sherif, (2008) “Enhanced swarm-like agents for dynamically adaptive data clustering”, Proceedings of the 2nd WSEAS International Conference on Computer Engineering and Applications, p.213-219, January 25-27, Acapulco, Mexico
Armstrong, J.S., Green, K.C., Soon, W.:“Polar Bear Population Forecasts: A Public-policy Forecasting Audit”; Interfaces, 38 (2008), 382-405.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.