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dc.contributor.authorCai, Liwei
dc.date.accessioned2014-09-29T16:10:53Z
dc.date.available2014-09-29T16:10:53Z
dc.date.issued1998-05-01
dc.identifier.urihttps://hdl.handle.net/11244/11939
dc.description.abstract"One gram ofknowledge is worth of tons of gold". The desire to obtain knowledge from collected data has generated a need for new techniques and tools that can transform the processed data into useful information and knowledge intelligently and automatically. Consequently, data mining has become a research area with increasing importance. Data mining, which is also known as knowledge discovery in databases, is the process of extracting and refining implicit, previously unknown and potentially useful knowledge from databases. Much research has set mining information and knowledge from databases as a key research topic. It also becomes an important area with major revenues opportunity for companies. Many researchers in the area of database systems, data warehousing, statistics, artificial intelligence, and expert systems have shown their interest in data mining. In response to such a demand a new data mining technique, the use of the perfect fixed point theorem is discussed and validated in this thesis. By using the perfect fixed point theorem in knowledge-bases, people can obtain some useful (previously unknown) information. It can help decision makers to make more accurate predications. Also, it can save time and money for decision makers and the researchers in other scientific and technology fields.
dc.formatapplication/pdf
dc.languageen_US
dc.publisherOklahoma State University
dc.rightsCopyright is held by the author who has granted the Oklahoma State University Library the non-exclusive right to share this material in its institutional repository. Contact Digital Library Services at lib-dls@okstate.edu or 405-744-9161 for the permission policy on the use, reproduction or distribution of this material.
dc.titleUse of the Fixed Point Theorem to Mine Data from a Knowledge-base
dc.typetext
osu.filenameThesis-1998-C133u.pdf
osu.accesstypeOpen Access
dc.type.genreThesis


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