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

Knowledge Discovery in Databases An OverviewAbstractIn the past, the term Data Mining was, and still is, used to designate the action mechanism of pulling useful information from databases. Now, this term is recognized to apply but to one activity in a very large process to bow out knowledge 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 come together under the umbrella of KDD.IntroductionToday, the topic of data mining has some(prenominal) interest in government, business, and research circles. With the harvest-tide of computer use within these areas has also come a greater desire to let the computers do the give-up the ghost that used to be done by humans. The problem, nowadays, is that the data that needs to be analyzed has become too large a nd cumbersome 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 embody of knowledge concerned with this process of data analysis. As with much 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 expanded upon almost a constant basis. The use of the Internet to distribute and collect 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 Man agement 232 (Winter 1997) pp.24-29.13) J. Quinlan, Induction of Decision Trees, railway car 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