Using an Interest Ontology for Improved Support in Rule Mining

Abstract

This paper describes the use of a concept hierarchy for improving the results of association rule mining. Given a large set of tuples with demographic information and personal interest information, association rules can be derived, that associate ages and gender with interests. However, there are two problems. Some data sets are too sparse for coming up with rules with high support. Secondly, some data sets with abstract interests do not represent the actual interests well. To overcome these problems, we are preprocessing the data tuples using an ontology of interests. Thus, interests within tuples that are very specific are replaced by more general interests retrieved from the interest ontology. This results in many more tuples at a more general level. Feeding those tuples to an association rule miner results in rules that have better support and that better represent the reality. 3 This research was supported by the NJ Commission for Science and Technology 3 Contact Author: James Geller,

Fuzzy Ontology based Approach for Flexible Association Rules Mining

“…Consequently, the power or cardinality of a finite fuzzy set A is given by the sum of the membership degrees of the elements belonging to fuzzy set A [9]. That is symbolically defined as in (4). Since an element can partially belong to a fuzzy set, a natural generalization of the classical notion of cardinality is to weigh each element by its membership degree, which resulted in the following formula for cardinality of a fuzzy set:…”

“…Also, the frequency of items or substitutes together are counted by one. Table (4) shows the result of association rules mining based on a crisp ontology to consider each items alternatives or substitutes. …”

“…The work described in [4], which uses ontology to improve support in rule mining, is an example of an approach that considers semantic information during the pre-processing step. In that work, data are raised to more generalised concepts according to the ontology, and then the mining process is performed by a conventional association rule mining algorithm, like Apriori [8].…”

“…Ontology specifies the concepts, relationships, and other distinctions that are related to modelling a domain to be shared between users [4,11]. In consequence, fuzzy ontology allows each object to be related to other objects in the ontology with a matching degree based on the fuzzy set theory invented by Zadeh [1].…”

“…Commonly, an ontology can be defined as "the conceptualization of a domain into a human understandable, machine readable format consisting of entities, attributes, relationships, and axioms" [4]. In other words, an ontology can be defined as the knowledge representation and common understanding of a domain.…”