Mining Association Rules with Ontological Information Ming-Cheng Tseng Institute of Information Engineering, I-Shou University, 840,Taiwan clark.tseng@msa.hinet.net Wen-Yang Lin Dept. of Comp. Sci. & Info. Eng., National University of Kaohsiung, 811, Taiwan wylin@nuk.edu.tw Rong Jeng Dept. of Information Management, I-Shou University, 840, Taiwan rjeng@isu.edu.tw Abstract The problem of mining association rules incorporated with domain knowledge has been studied recently. Previous work was conducted individually on two types of knowledge, classification and composition. In this paper, we revisit this problem from a more unified viewpoint. We consider the problem of mining association rules with ontological information that presents not only classification but also composition relationship. Two effective algorithms are proposed with empirical evaluation displayed. 1. Introduction It is well-known that the data mining process is knowledge intensive. It has been shown in many applications that with the aid of domain knowledge, one can discover more meaningful patterns and enrich the semantics of discovered rules. The most popular method in organizing the domain knowledge is employing ontology [3][8], which is an explicit specification of a conceptualization that can help us to define and share knowledge. One of the most important patterns in data mining is to discover association rules from a database. An association rule is an expression of the form, X  Y, where X and Y are sets of items. Such information is very useful in making decision for business management. In the past few years, there has been researches investigated the problem of mining association rules with classification or composition information [4][5][7], showing the benefit of incorporating domain knowledge and proposing effective algorithms. In this paper, we revisit this problem from a more unified viewpoint. We consider the problem of mining association rules with ontological information that presents not only classification but also composition relationship. Two effective algorithms are proposed with empirical evaluation displayed. The remaining of the paper is organized as follows. A review of related work is given in Section 2. The problem of mining association rules with ontology is formalized in Section 3. In Section 4, we describe the proposed methods for finding frequent itemsets. A simple example is provided for illustration. In Section 5, we present the experimental results. Finally, our conclusion is stated in the last section. 2. Related work Mining association rules in presence of taxonomy (classification hierarchy) information was first addressed in [6]. The problem is named as mining generalized association rules, which aims to find associations among items at any level of the taxonomy. Another closely related work but with different purpose was conducted in [4], which emphasized “ drill-down” discovery of association rules. In [5], Jea et al. considered the problem of discover multiple-level association rules with composition (has-a) hierarchy and proposed a method. Their approach is similar to [7]. In [2], Domingues and Rezende proposed an algorithm, called GART, which uses taxonomies, in the step of knowledge post-processing, to generalize and to prune uninteresting rules that may help the user to analyze the generated association rules. 3. Problem statement Let I  {i 1 , i 2 , …, i m } be a set of items, and DB  {t 1 , t 2 , …, t n } be a set of transactions, where each transaction t i  tid, A has a unique identifier tid and a set of items A (A  I). To study the mining of association rules with ontological information from DB, we assume that the ontology of items, T, is available and is denoted as a directed acyclic graph on I E, where E  {e 1 , e 2 , …, e p } represents the set of extended items derived from I,