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Knowledge Discovery in Databases
Knowledge discovery in databases involves the non-trivial extraction of implicit and previously unknown information from large amounts of data. As data and information doubles every 20 months, knowledge discovery is needed to help analyze growing data volumes and extract useful knowledge. Knowledge discovery aims to find certain, interesting, and efficient patterns in data and is related to approaches like database management, expert systems, statistics, and scientific discovery. It has many applications in fields such as science, marketing, investments, and fraud detection.
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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
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- 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.