Lín, V. (CZ) Dolejší, P. (CZ) Rauch, J. (CZ) Šimůnek, Milan (UIVT-O)
Source Title
Neural Network World. - : Ústav informatiky AV ČR, v. v. i.
- ISSN 1210-0552
Roč. 14, - (2004), s. 411-420
Number of pages
10 s.
Language
eng - English
Country
CZ - Czech Republic
Keywords
data mining ; contingency tables ; GUHA
Subject RIV
BA - General Mathematics
R&D Projects
LN00B107 GA MŠMT - Ministry of Education, Youth and Sports (MEYS)
Annotation
KL-Miner [9] is a datamining procedure that, given input data matrix M and a set of parameters, generates patterns of the form RxC/P. Here R and C are categorial attributes corresponding to the columns of M, and P is a Boolean condition defined in terms of the remaining columns of M. The pattern RxC/P means that R and C are strongly correlated on the submatrix of M formed by all the rows of M that satisfy P. In this paper, we mention the motivation that leads to designing of KL-miner, describing our new implementation of COLLAPS and giving application examples that illustrate the main features of KL-Miner.
