We propose a new framework of explanation-oriented data mining by adding an explanation construction and evaluation phase to the data mining process. While traditional approaches concentrate on mining algorithms, we focus on explaining mined results. The mining task can be viewed as unsupervised learning that searches for interesting patterns. The construction and evaluation of mined patterns can be formulated as supervised learning that builds explanations. The proposed framework is therefore a simple combination of unsupervised learning and supervised learning. The basic ideas are illustrated using association mining. The notion of conditional association is used to represent plausible explanations of an association. The condition in a conditional association explicitly expresses the plausible explanations of an association.
['Yiyu Yao Yao', 'Yan Zhao', 'R. B. Maguire']
Explanation-oriented association mining using a combination of unsupervised and supervised learning algorithms