Biskri, I.; Rompré, L.: Using association rules for query reformulation (2012)
0.00
0.0026402464 = product of:
0.023762217 = sum of:
0.023762217 = weight(_text_:of in 92) [ClassicSimilarity], result of:
0.023762217 = score(doc=92,freq=28.0), product of:
0.061262865 = queryWeight, product of:
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.03917671 = queryNorm
0.38787308 = fieldWeight in 92, product of:
5.2915025 = tf(freq=28.0), with freq of:
28.0 = termFreq=28.0
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.046875 = fieldNorm(doc=92)
0.11111111 = coord(1/9)
- 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.
