• Korean Management Science Review
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• v.24 no.2
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• pp.177-196
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• 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. Integrated models consist of four models: ASFM model which combines Association Rule(A) and Frequency Matrix(B), ASRI model which combines Association Rule(A) and Rule Induction(C), FMRI model which combines Frequency Matrix(B) and Rule Induction(C), and ASFMRI model which combines Association Rule(A), Frequency Matrix(B), and Rule Induction(C). 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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.11a
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• pp.489-498
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• 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
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• v.26 no.4
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• pp.477-491
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• 1997

A new predictive classification rule for assigning future cases into one of two multivariate normal population (with unknown normal mixture model) is considered. The development involves calculation of posterior probability of each possible normal-mixture model via a default Bayesian test criterion, called intrinsic Bayes factor, and suggests predictive distribution for future cases to be classified that accounts for model uncertainty by weighting the effect of each model by its posterior probabiliy. In this paper, our interest is focused on constructing the classification rule that takes care of uncertainty about the types of covariance matrices (homogeneity/heterogeneity) involved in the model. For the constructed rule, a Monte Carlo simulation study demonstrates routine application and notes benefits over traditional predictive calssification rule by Geisser (1982).

• Journal of the Korean Ceramic Society
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• v.40 no.4
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• pp.371-374
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• 2003

Various types of equation for mixing rule on permittivity of mixture have been proposed, but none of these is not perfect because of the inconsistency between the actual geometrical configuration and the basic model for calculation. Serial model and parallel model are lower and upper extremes of mixing manner, the apparent permittivity of any other type of mixture stay between these two extreme states. For the random mixture of the stumpy fine particles, customarily the logarithmic mixing rule has been applied. But, the logarithmic mixing rule does not give the proper value of permittivity in low or high mixing rate of constituent. The author proposed the new mixing rule that gives better consistency with measured value in whole mixing range compared to the logarithmic rule. In this paper, a desirable refinement on the equation proposed in the previous paper is made to adapt to thr configuration image of actual compound and then the equation has been expanded to the complex permittivity to apply the mixing rule on the dissipative materials cases.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.329-331
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• 1993

A new RMFSOC(Reference Model Following Self-Organizing Controller) is proposed. It is composed by adding the reference model and decision rule to the Mamdani's SOC. The reference model is introduced to explicitly specify the control performance. The self-organizing level of the RMFSOC organizes the control rule which makes the process output follow the reference output generated by the reference model. In order to avoid unnecessary control rule modification, a decision rule is also introduced to determine whether the control rule modification is needed or not.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.6
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• pp.10-21
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• 2013

This study suggested combined filtering model to eliminate outlier travel time data in transportation information system, and it was based on Median Absolute Deviation and Voting Rule. This model applied Median Absolute Deviation (MAD) method to follow normal distribution as first filtering process. After that, Voting rule is applied to eliminate remaining outlier travel time data after Median Absolute Deviation. In Voting Rule, travel time samples are judged as outliers according to travel-time difference between sample data and mean data. Elimination or not of outliers are determined using a majority rule. In case study of national highway No. 3, combined filtering model selectively eliminated outliers only and could improve accuracy of estimated travel time.

• Journal of information and communication convergence engineering
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• v.8 no.1
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• pp.89-94
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• 2010

The most important of rule-base system is the knowledge base that determines the power of rule-base system. The important form of this knowledge is how to descript kinds of rules. The Rule-Base System (RBS) has been using in many field that need reflect quickly change of business rules in management system. As far, when develop the Rule-Based System, we must make a rule engine with a general language. There are three disadvantage of in this developed method. First, while there are many data that must be processed in the system, the speed of processing data will become very slow so that we cannot accept it. Second, we cannot change the current system to make it adaptive to changes of business rules as quickly as possible. Third, large data make the rule engine become very complex. Therefore, in this paper, we propose the two important methods of raising efficiency of Rule-Base System. The first method refers to using the Relational database technology to process the rules of the Rule-Base System, the second method refers to a algorithm of according to Quine McCluskey formula compress the rows of rule table. Because the expressive languages of rule are still remaining many problems, we will introduce a new expressive language, which is Rule-Base Data Model short as RBDM in this paper.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.725-730
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• 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 Korean Society for Quality Management
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• v.29 no.1
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• pp.11-23
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• 2001

This paper is concerned with suggesting a Bayesian method for variable selection in multinomial logit model. It is based upon an optimal rule suggested by use of Bayes rule which minimizes a risk induced by selecting the multinomial logit model. The rule is to find a subset of variables that maximizes the marginal likelihood of the model. We also propose a Laplace-Metropolis algorithm intended to suggest a simple method forestimating the marginal likelihood of the model. Based upon two examples, artificial data and empirical data examples, the Bayesian method is illustrated and its efficiency is examined.

• Journal of Internet Computing and Services
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• v.1 no.1
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• pp.15-26
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• 2000

To efficiently manage data varying over time and process event driven transactions, some of the various database applications recently emerged require database systems supporting both a temporal data model and active rule processing. There has been much progress in independent research on temporal databases and active databases, but studies on databases which support both functions, have been rare. In this paper, an active temporal rule model supporting both active rule processing and temporal data model is presented with its rule expression language. This active temporal rule model contributes to the active function extension of the temporal database, and to establishing the concept of data access events which refer temporal attributes of data in active rules.