Mining knowledge from object-oriented instances

Authors:
Cheng-Ming Huang;Tzung-Pei Hong;Shi-Jinn Horng
Affiliations:
Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan, ROC;Department of Electrical Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan, ROC;Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan, ROC and Department of Electronic Engineering, National United ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2007

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Abstract

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Recently, the object concept has been very popular and used in a variety of applications, especially for complex data description. This paper thus proposes a new data-mining algorithm for extracting interesting knowledge from transactions stored as object data. Each item itself is thought of as a class, and each item purchased in a transaction is thought of as an instance. Instances with the same class (item name) may have different attribute values since they may appear in different transactions. The proposed algorithm is divided into two main phases, one for intra-object association rules, and the other for inter-object association rules. Two apriori-like procedures are adopted to find the two kinds of rules. The first phase finds out the association relation within the same kind of objects. Each large itemset found in this phase can be thought of as a composite item used in phase 2. The second phase then finds the relationship among different kinds of objects. Both the intra-object and inter-object association rules can thus be easily derived by the proposed algorithm at the same time. Experiments are also made to show the effect of the proposed algorithm.