Showing papers on "Concept mining published in 1995"
02 Dec 1995
TL;DR: Methods for mining knowledge at multiple concept levels can often be developed by extension of existing data mining techniques, and it is often necessary to adopt techniques such as step-by-step generalization/specialization or progressive deepening of a knowledge mining process.
Abstract: Most studies on data mining have been focused at mining rules at single concept levels, i.e,, either at the primitive level or at a rather high concept level. However, it is often desirable to discover knowledge at multiple concept levels. Mining knowledge at multiple levels may help database users find some interesting rules which are difficult to be discovered otherwise and view database contents at different abstraction levels and from different angles. Methods for mining knowledge at multiple concept levels can often be developed by extension of existing data mining techniques. Moreover, for eficient processing and interactive mining of multiple-level rules, it is often necessary to adopt techniques such as step-by-step generalization/specialization or progressive deepening of a knowledge mining process. Other issues, such as visual representation of knowledge at multiple levels, and “redundant” rule filtering, should also be studied in depth.
82 citations
20 Mar 1995
TL;DR: "Data mining" is a technique to extract nontrivial regularities or relationships in databases to provide users with a very powerful tool for exploiting vast amount of stored data.
Abstract: "Data mining" is a technique to extract nontrivial regularities or relationships in databases. The purpose of data mining is to extract nontrivial regularities or relationships as a piece of knowledge in databases. This technique can provide users with a very powerful tool for exploiting vast amount of stored data. >
20 citations