Implementation Density Based clustering in Data Mining
Keywords:
retrospective, analyses, warehouses, predictiveAbstract
Data mining extraction of hidden predictive info from large database records, is a powerful new technology with great potential to help companies focus on most important info within their data value warehouses. Data mining utensils predict future trends &behaviors, permitting businesses to make taking initiative, knowledge motivated decisions. Automated, prospective analyses offered by data mining transfer outside analyses of past events providing by retrospective utensils typical of decision support systems. data mining utensils may answer business questions that usually were too time consuming to resolve. They scour database record records for hidden patterns, finding predictive info that experts may miss since this lies outside their expectations.
References
Mr. Dishek Mankad “The Study on Data Warehouse Design & Usage” International Journal of Scientific & Research Publications , Volume 3, Issue 3, March 2013 ISSN 2250- 3153
Surajit Chaudhuri wrote on An Overview of Data Warehousing & OLAP Technology (Appears in ACM Sigmod Record, March 1997).
Manjunath T. N. wrote on Realistic Analysis of Data Warehousing & Data Mining Application in Education Domain
Weiss, Sholom M.; & Indurkhya, Nitin (1998); Predictive Data Mining, Morgan Kaufmann
Kimball, R.The Data Warehouse Toolkit. John Wiley, 1996.
Barclay, T., R. Barnes, J. Gray, P. Sundaresan, “Loading Databases using Dataflow Parallelism.” SIGMOD Record, Vol.23, No. 4, Dec.1994.
Blakeley, J.A., N. Coburn, P. Larson. “Updating Derived Relations: Detecting Irrelevant & Autonomously ComputableUpdates.” ACM TODS, Vol.4, No. 3, 1989.
Gupta, A., I.S. Mumick, “Maintenance of Materialized Views: Problems, Techniques, & Applications.” Data Eng. Bulletin, Vol. 18, No. 2, June 1995. 9 Zhuge, Y., H. Garcia-Molina, J. Hammer, J. Widom, “View Maintenance in a Warehousing Environment, Proc. Of SIGMOD Conf., 1995.
Roussopoulos, N., et al., “The Maryland ADMS Project: Views R Us.” Data Eng. Bulletin, Vol. 18, No.2, June 1995.[11] O’Neil P., Quass D. “Improved Query Performance withVariant Indices”, To appear in Proc. of SIGMOD Conf., 1997.
O’Neil P., Graefe G. “Multi-Table Joins through BitmappedJoin Indices” SIGMOD Record, Sep 1995.
Harinarayan V., Rajaraman A., Ullman J.D. “ Implementing Data Cubes Efficiently” Proc. of SIGMOD Conf., 1996.
Chaudhuri S., Krishnamurthy R., Potamianos S., Shim K.“Optimizing Queries with Materialized Views” Intl.Conference on Data Engineering, 1995.
Levy A., Mendelzon A., Sagiv Y. “Answering Queries Using Views” Proc. of PODS, 1995. 16 Yang H.Z., Larson P.A. “Query Transformations for PSJ Queries”, Proc. of VLDB, 1987
Witten, Ian H.; Frank, Eibe; Hall, Mark A. (30 January 2011). Data Mining: Practical Machine Learning Tools & Techniques (3 ed.). Elsevier. ISBN 978-0-12-374856-0.
Ye, Nong (2003); Handbook of Data Mining, Mahwah, NJ: Lawrence Erlbaum
Cabena, Peter; Hadjnian, Pablo; Stadler, Rolf; Verhees, Jaap; Zanasi, Alessandro (1997); Discovering Data Mining: From Concept to Implementation, Prentice Hall, ISBN 0-13-743980-6
M.S. Chen, J. Han, P.S. Yu (1996) "Data mining: an overview from a database perspective". Knowledge & data Engineering, IEEE Transactions on 8 (6), 866–883
Feldman, Ronen; Sanger, James (2007); Text Mining Handbook, Cambridge University Press, ISBN 978-0-521-83657-9
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