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Use of the Fixed Point Theorem to Mine Data from a Knowledge-base

Cai, Liwei
"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.