Knowledge Discovery in Databases: An Overview Essay -- Data Mining

K directlyledge Discovery in Databases An OverviewAbstractIn the past, the term Data minelaying was, and still is, used to designate the activity of pulling useful information from databases. Now, this term is recognized to apply but to one activity in a very queen-size process to extract knowledge from opaque databases. The overall process is known as Knowledge Discovery in Databases, (KDD). This process is comprised of many an(prenominal) subprocesses which when linked together provide a firm foundation for knowledge acquisition from large databases. Many tools, techniques, and disciplines come together under the umbrella of KDD. understructureToday, the topic of data mining has much interest in government, business, and research circles. With the growth of computer use within these areas has also come a greater desire to let the computers do the work that used to be done by humans. The problem, nowadays, is that the data that needs to be analyzed has become too large and cumbe rsome for one person or even teams of people to envision tackling without help from computers. These computers are no longer mere crunchers of numbers but now they find the patterns that the humans used to find. From this growth has arisen a vast body of knowledge concerned with this process of data analysis. As with much other information, the cyberspace is employed to make available the ever-growing body of information on this topic. Many general sources of information a,b,c are now online. These are updated and expanded upon almost a constant basis. The use of the Internet to disseminate and collect information is itself a consideration in this field. The amount of information is expanding at such a reckon that old methods of information disposal, such as paper journals and b... ...11) R. Lippman, An Introduction to Computing with Neural Networks, IEEE ASSP Magazine 42 (1987), pp.4-22.12) C. Murphy, G. Koehler & H. Fogler, Artifical Stupidity, The Journal of Portfolio Manageme nt 232 (Winter 1997) pp.24-29.13) J. Quinlan, Induction of Decision Trees, Machine Learning 11 (1986), pp.81-106.Hyperlinksa) http//www.cs.bham.ac.uk/anp/TheDataMine.htmlb) http//www.gmd.de/ml-archivec) http//info.gte.com/kdd/d) http//info.gte.com/kdd/corporate.htmle) http//info.gte.com/kdd/datasets.htmlf) http//info.gte.com/kdd/siftware.htmlg) http//www.almaden.ibm.com/stss/h) http//www.research.microsoft.com/research/datamine/i) http//www-aig.jpl.nasa.gov/kdd95/j) http//www-aig.jpl.nasa.gov/kdd96/k) http//www.neuronet.ph.kcl.ac.uk/l) http//www.ics.uci.edu/AI/ML/Machine-Learning.html