• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2007.11a
• /
• pp.489-498
• /
• 2007

For better predictions and classifications in customer recommendation, this study proposes an integrative model that efficiently combines the currently-in-use statistical and artificial intelligence models. In particular, by integrating the models such as Association Rule, Frequency Matrix, and Rule Induction, this study suggests an integrative prediction model. The data set for the tests is collected from a convenience store G, which is the number one in its brand in S. Korea. This data set contains sales information on customer transactions from September 1, 2005 to December 7, 2005. About 1,000 transactions are selected for a specific item. Using this data set, it suggests an integrated model predicting whether a customer buys or not buys a specific product for target marketing strategy. The performance of integrated model is compared with that of other models. The results from the experiments show that the performance of integrated model is superior to that of all other models such as Association Rule, Frequency Matrix, and Rule Induction.

• Journal of the Korean Statistical Society
• /
• v.16 no.1
• /
• pp.26-36
• /
• 1987

In this paper, we study on a subset selection procedure based on Hodges-Lehmann estimators derived from the Wilcoxon test. To estimate the standard error of the Hodges-Lehmann estimators, the biweight A-estimator of scale is used. The Pitman efficiency of the proposed rule is compared with the Gupta's rule and the trimmed-means rule through a small-sample Monte Carlo study. The results show that the proposed rule satisfies the $P^*$-condition and is very efficient in various heavy-tailed distributions.

• 한국데이터정보과학회:학술대회논문집
• /
• 2003.10a
• /
• pp.197-213
• /
• 2003

One of the well-studied problems in data mining is the search for association rules. The goal of association rule mining is to find all the rules with support and confidence exceeding some user specified thresholds. In this paper we consider the relation between the measure of association and the criteria of association rule for ordinal data.

• The KIPS Transactions:PartB
• /
• v.10B no.1
• /
• pp.27-32
• /
• 2003

The rule induction system generates a set of inductive rules, and the task of selecting an optimal rule subset is one of the important problem in the area of rule induction. This paper proposes a new learning method which combines rule induction system with the paradigm of genetic algorithm. This paper shows that genetic algorithm can be effectively applied to optimal rule selection problem. The proposed system was evaluated using a set of different machine learning data sets and, showed better performance in all cases than other traditional methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.06a
• /
• pp.725-730
• /
• 1998

Recently we extended the fuzzy model for rule based systems incorporating an importance factor for each rule. The model permits for both unrestricted as well as non-negative importance factors. We use this extended model to design a fuzzy rule based classifier system which uses both the firing strength of the rule and the importance factor to decide the class label. The effectiveness of the scheme is established using several data sets.

• Journal of applied mathematics & informatics
• /
• v.13 no.1_2
• /
• pp.365-372
• /
• 2003

We show that if ${\gamma}$ $\leq$ 2, the average error of the composite Trapezoidal rule on two consecutive intervals is proportional to h$\^$2h+3/ where h is the length of each subinterval of the interval [0, 1]. As a result, we show that the Trapezoidal rule with equally spaced points is optimal in the average case setting when ${\gamma}$ $\leq$ 2.

• Communications of the Korean Mathematical Society
• /
• v.13 no.4
• /
• pp.903-911
• /
• 1998

We show that if r $\leq$ 2, the average error of the Trapezoidal rule is proportional to $n^{-min{r＋l, 3}}$ where n is the number of mesh points on the interval [D, 1]. As a result, we show that the Trapezoidal rule with equally spaced points is optimal in the average case setting when r $\leq$ 2.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.1054-1057
• /
• 1993

A direct method of fuzzy inference and a fuzzy algorithm with learning function are applied to the steam generator level control of nuclear power plant. The fuzzy controller by use of direct inference can control the steam generator in the entire range of power level. There is a little long response time of fuzzy direct inference controller at low power level. The rule base of fuzzy controller with learning function is divided into two parts. One part of the rule base is provided to level control of steam generator at low power level (0%∼30% of full power). Response time of steam generator level control at low power level with this rule base is shown generator level control at low power level with this rule base is shown to be shorter than that of fuzzy controller with direct inference.

• Journal of the Korea Society for Simulation
• /
• v.7 no.1
• /
• pp.39-51
• /
• 1998

In this paper, MVED (Minimum Vehicle's Empty Distance), a new dispatching rule for a Single Loop-Single Vehicle AGV system, is developed. Single Loop-Single Vehicle AGV system consists of segmented bi-directional single loops, in which only one AGV is assigned. The MVED rule is focused on minimizing vehicles empty travel distance. Using simulation, the performance of the MVED is compared with that of existing dispatching rules such as STT/D and MEVTT. The results show MVED rule's better performance than others. ARENA is used as a simulation language.

• Journal of Intelligence and Information Systems
• /
• v.8 no.1
• /
• pp.141-157
• /
• 2002

extensible Markup Language (XML) is a new markup language for data exchange on the Internet. In this paper, we propose a language extensible Rule Markup Language (XRML) which is an extension of XML. The implicit rules embedded in the Web pages should be identifiable, interchangeable with structured rule format, and finally accessible by various applications. It is possible to realize by using XRML. In this light, Web based Knowledge Management Systems (KMS) can be integrated with rule-based expert systems. To meet this end, we propose the six design criteria: Expressional Completeness, Relevance Linkability, Polymorphous Consistency, Applicative Universality, Knowledge Integrability and Interoperability. Furthermore, we propose three components such as RIML (Rule Identification Markup Language), RSML (Rule Structure Markup Language) and RTML (Rule Triggering Markup Language), and the Document Type Definition DTD). We have designed the XRML version 0.5 as illustrated above, and developed its prototype named Form/XRML which is an automated form processing for disbursement of the research fund in the Korea Advanced Institute of Science and Technology (KAISI). Since XRML allows both human and software agent to use the rules, there is huge application potential. We expect that XRML can contribute to the progress of Semantic Web platforms making knowledge management and e-commerce more intelligent. Since there are many emerging research groups and vendors who investigate this issue, it will not take long to see XRML commercial products. Matured XRML applications may change the way of designing information and knowledge systems in the near future.