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Data Archeology "Knowledge Discovery in Databases"

Affiliations

  • G.H Raisoni College of Engineering and Management, Pune

Abstract


In the present era challenging problems cannot be solved in a reasonable amount of time with conventional computers. While grand challenge problems can be found in many domains like, computing software changes, science applications etc. These problems often require numerous complex calculations and collaboration among people with multiple disciplines and geographic locations. Many grand challenge problems involve the analysis of very large volumes of data. Data mining, also popularly known as Knowledge Discovery in Databases (KDD) is a well established field of computer science concerned with the automated search of large volumes of data for patterns that can be considered knowledge about the data. Data Archeology offers automated discovery of previously unknown patterns as well as automated prediction of trends and behaviors; its technologies are complimentary to existing decision support tools and provide the business analyst and marketing professional with a new way of analyzing the business. After a general introduction of the knowledge discovery, this paper concludes the applications of data mining and steps, stages, techniques of knowledge discovery have been presented.

Keywords

Bayesian, Data Mining, Exploration, Knowledge

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References


  • Buntine, W., (1996), Graphical Models for Discovering Knowledge, Advances in Knowledge Discovery and Data Mining, 59-82.
  • Buntine, W,(1996),A Guide To The Literature On Learning Probabilistic Networks From Data, IEEE Transactions on Knowledge and Data Engineering 8(2), 195-210
  • Guyon, I., Matic, N., Vapnik, V., (1996), Discovering Informative Patterns and Data Cleaning, Advances, 181-203.
  • Hsu, C.N., Knoblock, C.A., (1996), Using Inductive Learning to Generate Rules for Semantic Query Optimization, Advances in Knowledge Discovery and Data Mining, 425-445.
  • Quinlan, J.R.,(1993),Programs For Machine Learning,4(5).

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