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

K at a timeledge baring in selective informationbases An OverviewAbstractIn the past, the term Data Mining was, and still is, enforced to designate the performance of pulling multipurpose information from databases. Now, this term is recognized to apply but to one activity in a very large process to extract noesis from opaque databases. The overall process is known as Knowledge Discovery in Databases, (KDD). This process is comprised of many subprocesses which when linked together provide a firm foundation for knowledge acquisition from large databases. Many tools, techniques, and disciplines seed together under the umbrella of KDD.IntroductionToday, the topic of data mine 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 permit 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 incompetent for one person or even teams of people to envision tackling without abet 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 some other information, the Internet 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 grow upon almost a constant basis. The use of the Internet to disseminate and view information is itself a consideration in this field. The amount of information is expanding at such a rate 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 Mana gement 232 (Winter 1997) pp.24-29.13) J. Quinlan, Induction of purpose 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