Biskri, I.; Rompré, L.: Using association rules for query reformulation (2012)
0.00
2.0495258E-4 = product of:
0.0047139092 = sum of:
0.0047139092 = product of:
0.0094278185 = sum of:
0.0094278185 = weight(_text_:1 in 92) [ClassicSimilarity], result of:
0.0094278185 = score(doc=92,freq=2.0), product of:
0.057894554 = queryWeight, product of:
2.4565027 = idf(docFreq=10304, maxDocs=44218)
0.023567878 = queryNorm
0.16284466 = fieldWeight in 92, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
2.4565027 = idf(docFreq=10304, maxDocs=44218)
0.046875 = fieldNorm(doc=92)
0.5 = coord(1/2)
0.04347826 = coord(1/23)
- Abstract
- In this paper the authors will present research on the combination of two methods of data mining: text classification and maximal association rules. Text classification has been the focus of interest of many researchers for a long time. However, the results take the form of lists of words (classes) that people often do not know what to do with. The use of maximal association rules induced a number of advantages: (1) the detection of dependencies and correlations between the relevant units of information (words) of different classes, (2) the extraction of hidden knowledge, often relevant, from a large volume of data. The authors will show how this combination can improve the process of information retrieval.