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Knowledge Discovery in Databases

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Diwas Kandel
Diwas Kandel

Knowledge Discovery

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Knowledge Discovery in Databases By: Diwas Kandel- 17618843 Type B
Knowledge Discovery • Knowledge discovery is the nontrivial extraction of implicit, previously unknown, and potentially useful, information from data. • Exponentially increasing data/information • Hard to analyse the data due to its increasing volume.
Knowledge Discovery • It has been estimated that the amount of information in the world doubles every 20 months. • Characteristics of Knowledge Discovery • Certainty • Interesting • Efficiency
Comparison of various terms
Related Approaches • Database management • Expert Systems • Statistics • Scientific Discovery
Need of KDD • There is an urgent need for a new generation of computational theories and tools to assist humans in Extracting useful Information (knowledge) from the rapidly growing volumes of digital data. • Use in various fields such as science and businesses • Marketing • Investment • Fraud Detection • Telecommunications
Review of Papers • Norton, M. J. (1999). Knowledge discovery in databases. Library Trends, 48(1), 9-21. • Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37. • Frawley, W. J., Piatetsky-Shapiro, G., & Matheus, C. J. (1992). Knowledge discovery in databases: An overview. AI magazine, 13(3), 57.
Knowledge Discovery in Databases

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Knowledge Discovery in Databases

  • 1. Knowledge Discovery in Databases By: Diwas Kandel- 17618843 Type B
  • 2. Knowledge Discovery • Knowledge discovery is the nontrivial extraction of implicit, previously unknown, and potentially useful, information from data. • Exponentially increasing data/information • Hard to analyse the data due to its increasing volume.
  • 3. Knowledge Discovery • It has been estimated that the amount of information in the world doubles every 20 months. • Characteristics of Knowledge Discovery • Certainty • Interesting • Efficiency
  • 5. Related Approaches • Database management • Expert Systems • Statistics • Scientific Discovery
  • 6. Need of KDD • There is an urgent need for a new generation of computational theories and tools to assist humans in Extracting useful Information (knowledge) from the rapidly growing volumes of digital data. • Use in various fields such as science and businesses • Marketing • Investment • Fraud Detection • Telecommunications
  • 7. Review of Papers • Norton, M. J. (1999). Knowledge discovery in databases. Library Trends, 48(1), 9-21. • Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37. • Frawley, W. J., Piatetsky-Shapiro, G., & Matheus, C. J. (1992). Knowledge discovery in databases: An overview. AI magazine, 13(3), 57.