NEW APPROACHES FOR DISCOVERING EXCEPTION AND ANOMALOUS RULES

作者：Delgado Miguel^*; Dolores Ruiz M; Sanchez Daniel

来源：International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 2011, 19(2): 361-399.

DOI：10.1142/S0218488511007039

• Data mining
• association rules
• exception rules
• anomalous rules
• double rules
• logic model

Mining association rules is a well known framework for extracting useful knowledge from databases. Despite their proven applicability there exist other approaches that also search for novel and useful information such us peculiarities, infrequent rules, exceptions or anomalous rules. The common feature of these proposals is the low support of such type of rules. So there is a necessity of finding efficient algorithms for extracting them.
The principal objective of this paper is providing a unified framework for dealing with such kind of rules. In our case, we take advantage of an existing logic approach called GUHA. This model was first presented in the middle sixties by Hajek et al. and then has been developed by Rauch and others in the last decade.
Following this line, this paper also offers some interesting issues. First, it provides a. deep analysis of semantics and formulation of exception and anomalous rules. Second, we define the so called double rules as a new type of rules which in conjunction with exceptions and anomalies will describe in more detail the relationship between two net of items. Third, we give new approaches for mining them and we propose an algorithm with reasonably good performance.

• 出版日期2011-4