Biskri, I.; Rompré, L.: Using association rules for query reformulation (2012)
0.00
0.0012307836 = product of:
0.0067693098 = sum of:
0.0046860883 = weight(_text_:a in 92) [ClassicSimilarity], result of:
0.0046860883 = score(doc=92,freq=8.0), product of:
0.030653298 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.026584605 = queryNorm
0.15287387 = fieldWeight in 92, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046875 = fieldNorm(doc=92)
0.0020832212 = weight(_text_:s in 92) [ClassicSimilarity], result of:
0.0020832212 = score(doc=92,freq=2.0), product of:
0.028903782 = queryWeight, product of:
1.0872376 = idf(docFreq=40523, maxDocs=44218)
0.026584605 = queryNorm
0.072074346 = fieldWeight in 92, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.0872376 = idf(docFreq=40523, maxDocs=44218)
0.046875 = fieldNorm(doc=92)
0.18181819 = coord(2/11)
- 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.
- Pages
- S.291-303
- Type
- a
