Fast extraction of gradual association rules
Lisa Di Jorio, Anne Laurent, Maguelonne Teisseire · 2008
Even if they have proven to be relevant on traditional transactional databases, data mining tools are still inefficient on some kinds of databases. In particular, databases containing discrete values or having a value for each item, like gene expression data, are especially challenging. On such data, existing approaches either transform the data to classical binary attributes, or use discretisation, including fuzzy partition to deal with the data. However, binary mapping of such databases drives to a loss of information and extracted knowledge is not exploitable for end-users. Thus, powerful tools designed for this kind of data are needed. On the other hand, existing fuzzy approaches hardly take gradual notions into account, or are not scalable enougth to tackle the problem.