The Irrelevant Values Problem of Decision Tree for Improving a Glass Sputtering Process

[[abstract]]In this paper, we use decision tree to establish a yield improvement model for glass sputtering process; however, the tree may have irrelevant values problem. In other words, when the tree is represented by a set of rules, not only comprehensibility of the resultant rules will be detracted but also critical factors of the manufacturing process cannot be effectively identified. From the performance issue and practical issue, we have to remove irrelevant conditions from the rules; otherwise, a domain expert is needed to review the decision tree. In this paper, we use a very simple example to demonstrate this point of view. Moreover, to identify and remove irrelevant conditions from the rules, we also revise Chiang's previous algorithm such that the modified algorithm can deal not only discrete data but also quantitative data.[[notice]]補正完畢[[incitationindex]]EI[[booktype]]紙

Analysis on repeat-buying patterns

100學年度研究獎補助論文[[abstract]]Consumer market has several characteristics in common such as repeat-buying over the relevant time frame, a large number of customers, and a wealth of information detailing past customer purchases. Analyzing the characterizations of repeat-buying is necessary to understand and adapt to dynamics of customer behaviors for company to survive in a continuously changing environment. The aim of this paper is to develop a methodology to detect the existence of repeat-buying behavior and discover the potential period of the repeat-buying behavior. We propose a new mathematical model to capture the characteristics of repeat-buying behavior. The algorithms based on our previous works then proposed to provide a scheme to discover periodicity and trends of the purchase. Two fundamental repeat-buying types have been identified and analyzed. Any repeat-buying scenarios can be expressed as the combination of the two fundamental types. The proposed mathematical model coupled with our prior works on cyclic modeling form a systematic process to uncover the characteristics of repeat-buying phenomenon. The experiments against a domestic consumer goods company are provided. The experimental results show that the proposed model can predict likely periodic purchase more precisely than previous studies.[[incitationindex]]SCI[[booktype]]紙

Mining Disjunctive Consequent Association Rules

100學年度研究獎補助論文[[abstract]]When associationrules A → B and A → C cannot be discovered from the database, it does not mean that A → B ∨ C will not be an associationrule from the same database. In fact, when A, B or C is the newly marketed product, A → B ∨ C shall be a very useful rule in some cases. Since the consequent item of this kind of rule is formed by a disjunctive composite item, we call this type of rules as the disjunctiveconsequentassociationrules. Therefore, we propose a simple but efficient algorithm to discover this type of rules. Moreover, when we apply our algorithm to insurance policy for cross selling, the useful results have been proven by the insurance company.[[incitationindex]]SCI[[booktype]]紙