Benoit, G.: Data mining (2002)
0.00
0.003091229 = product of:
0.012364916 = sum of:
0.012364916 = weight(_text_:information in 4296) [ClassicSimilarity], result of:
0.012364916 = score(doc=4296,freq=6.0), product of:
0.06134496 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.034944877 = queryNorm
0.20156369 = fieldWeight in 4296, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046875 = fieldNorm(doc=4296)
0.25 = coord(1/4)
- Abstract
- Data mining (DM) is a multistaged process of extracting previously unanticipated knowledge from large databases, and applying the results to decision making. Data mining tools detect patterns from the data and infer associations and rules from them. The extracted information may then be applied to prediction or classification models by identifying relations within the data records or between databases. Those patterns and rules can then guide decision making and forecast the effects of those decisions. However, this definition may be applied equally to "knowledge discovery in databases" (KDD). Indeed, in the recent literature of DM and KDD, a source of confusion has emerged, making it difficult to determine the exact parameters of both. KDD is sometimes viewed as the broader discipline, of which data mining is merely a component-specifically pattern extraction, evaluation, and cleansing methods (Raghavan, Deogun, & Sever, 1998, p. 397). Thurasingham (1999, p. 2) remarked that "knowledge discovery," "pattern discovery," "data dredging," "information extraction," and "knowledge mining" are all employed as synonyms for DM. Trybula, in his ARIST chapter an text mining, observed that the "existing work [in KDD] is confusing because the terminology is inconsistent and poorly defined.
- Source
- Annual review of information science and technology. 36(2002), S.265-312
