• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.366-370
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• 2003

In this research, we suggest a hybrid intelligent Web recommendation systems based on Web data mining and case-based reasoning (CBR). One of the important research topics in the field of Internet business is blending artificial intelligence (AI) techniques with knowledge discovering in database (KDD) or data mining (DM). Data mining is used as an efficient mechanism in reasoning for association knowledge between goods and customers＇ preference. In the field of data mining, the features, called attributes, are often selected primary for mining the association knowledge between related products. Therefore, most of researches, in the arena of Web data mining, used association rules extraction mechanism. However, association rules extraction mechanism has a potential limitation in flexibility of reasoning. If there are some goods, which were not retrieved by association rules-based reasoning, we can＇t present more information to customer. To overcome this limitation case, we combined CBR with Web data mining. CBR is one of the AI techniques and used in problems for which it is difficult to solve with logical (association) rules. A Web-log data gathered in real-world Web shopping mall was given to illustrate the quality of the proposed hybrid recommendation mechanism. This Web shopping mall deals with remote-controlled plastic models such as remote-controlled car, yacht, airplane, and helicopter. The experimental results showed that our hybrid recommendation mechanism could reflect both association knowledge and implicit human knowledge extracted from cases in Web databases.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.19-22
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• 2022

In this work, we studied consumers' attractiveness/usefulness perceptions (CAUP) of online commerce product photos when exposed to alternative dark/light user interface (UI) modes. We analyzed time-series EEG data from 31 individuals and performed neuroscience mining (NSM) to ascertain (a) how the CAUP of products differs among UI modes; and (b) which deep learning model provides the most accurate assessment of such neuroscience mining (NSM) business difficulties. The dark UI style increased the CAUP of the products displayed and was predicted with the greatest accuracy using a unique EEG power spectra separated wave brainwave 2D-ConvLSTM model. Then, using relative importance analysis, we used this model to determine the most relevant power spectra. Our findings are considered to contribute to the discovery of objective truths about online customers' reactions to various user interface modes used by various online marketplaces that cannot be uncovered through more traditional research approaches like as surveys.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.1
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• pp.133-140
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• 2011

For both customers and companies, it is very important to analyze online customer reviews, which consist of small documents that include opinions or experiences about products or services, because the customers can get good informations and the companies can establish good marketing strategies. In this paper, we propose the association model for the opinion mining which can analyze customer opinions posted on web. The association model is to modify the association rules mining model in data mining in order to apply efficiently and effectively the association mining techniques to the opinion mining. We designed and implemented the opinion mining systems based on the modified association model and the grouping idea which would enable it to generate significant rules more.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.171-176
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• 2002

The Internet, as a commercial tool, provided a new market that connects producers to consumers through I-commerce. 2-commerce through the Internet became a new trend in all industries. This research indicates problems that block the activation of I-commerce of agricultural products, which is less developed than the other industries. To solve the problems it suggests E-commerce for agricultural products combining on and off line markets. It also suggests data mining technique for analyzing entire information in system.

• International Journal of Reliability and Applications
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• v.3 no.4
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• pp.155-164
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• 2002

This paper focuses on how modem data mining can be integrated with real-time relational databases and commercial data warehouses to improve reliability in real-time. An important Issue for many manufacturers is the development of relational databases that link key product attributes with real-time process parameters. Helpful data for key product attributes in manufacturing may be derived from destructive reliability testing. Destructive samples are taken at periodic time intervals during manufacturing, which might create a long time-gap between key product attributes and real-time process data. A case study is briefly summarized for the medium density fiberboard (MDF) industry. MDF is a wood composite that is used extensively by the home building and furniture manufacturing industries around the world. The cost of unacceptable MDF was as large as 5% to 10% of total manufacturing costs. Prevention can result In millions of US dollars saved by using better Information systems.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.139-157
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• 2002

The rapid growth of e-commerce has made both companies and customers face a new situation. Whereas companies have become to be harder to survive due to more and more competitions, the opportunity for customers to choose among more and more products has increased. So, the recommender systems that recommend suitable products to the customer have an important position in E-commerce. This research introduces collaborative filtering based recommender system which helps customers find the products they would like to purchase by producing a list of top-N recommended products. The suggested methodology is based on decision tree, product taxonomy, and association rule mining. Decision tree is used to select target customers, who have high possibility of purchasing recommended products. We applied the recommender system to a Korean department store. The methodology is evaluated with the analysis of a real department store case and is compared with other methodologies.

• The Journal of Information Systems
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• v.22 no.3
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• pp.25-42
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• 2013

Recently various kinds of Information Technology services are created and the quantities of the data flow are increase rapidly. Not only that, but the data patterns that we deal with also slowly becoming diversity. As a result, the demand of discover the meaningful knowledge/information through the various mining analysis such as linkage analysis, sequencing analysis, classification and prediction, has been steadily increasing. However, solving the business problems using data mining analysis does not always concerning, one of the major causes of these limitations is there are some analyzed data can't accurately reflect the real world phenomenon. For example, although the time gap of purchasing the two products is very short, by using the traditional sequencing analysis, the precedence relationship of the two products is clearly reflected. But in the real world, with the very short time interval, the precedence relationship of the two purchases might not be defined. What was worse, the sequence of the purchase intention and the sequence of the purchase realization of the two products might be mutually be reversed. Therefore, in this study, an expanded sequencing analysis methodology has been proposed in order to reflect this situation. In this proposed methodology, the purchases that being made in a very short time interval among the purchase order which might not important will be notice, and the analysis which included the original sequence and reversed sequence will be used to extend the analysis of the data. Also, to some extent a very short time interval can be defined as the time interval, so an experiment were carried out to determine the varying based on the time interval for the actual data.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.611-612
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• 2006

Many data mining techniques have been proved useful in revealing important patterns from large data sets. Especially, data mining techniques play an important role in a customer data analysis in a financial industry and an electronic commerce. Also, there are many data mining related research papers in a semiconductor industry and an automotive industry. In addition, data mining techniques are applied to the bioinformatics area. To satisfy customers' various requirements, each industry should develop new processes with more accurate production criteria. Also, they spend more money to guarantee their products' quality. In this manner, we apply data mining techniques to the production-related data such as a test data, a field claim data, and POP (point of production) data in the automotive parts industry. Data collection and transformation techniques should be applied to enhance the analysis results. Also, we classify various types of manufacturing processes and proposed an analysis scheme according to the type of manufacturing process. As a result, we could find inter- or intra-process relationships and critical features to monitor the current status of the each process. Finally, it helps an industry to raise their profit and reduce their failure cost.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.608-613
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• 2006

There are many e-showing mall because of revitalization of e-commerce system. It is necessary to recommending system of products that is for saving time and effort of customer. In this paper, we propose the system that is applying classification among data mining techniques to analysis of log data of customer. This log data contains access of user and purchasing of products. The proposed system operates in two phases. The first phase is composed of data filter module and association extraction module among web pages. The second phase is composed of personalization module and rule generation module. Customer can easily know the recommended sites because the proposed system can present rank of the recommended web pages to customer. As a result, the proposed system can efficiently do recommending of products to customer.

• Economic and Environmental Geology
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• v.47 no.3
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• pp.237-246
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• 2014

In this study, it is estimated how many changes of macroeconomic variables are happened under the proposition of import substitution of mining products 1% using macroeconometric input-output model. For this, used macroeconometric input-output model is composed of 141 behavioral equations representing the macroeconomy structure. In general, macroeconometrics models are constructed mainly on the side of the expenditure then it is not easy to estimate the effects of the shocks occurred from industry level. To mitigate that, this study tries to construct a macroeconometric input-output model. Macroeconometrics model which is useful to estimate the effects of macroeconomic shocks, economic policy and more, in this study, is linked with input-output table through the NDI(national disposable income) derived from compensation of employee. And this paper presents the estimation results of import substitution effects of mining products on Korean economy. As a results, GDP is increased 0.00073%, gross labor employed 0.00029%, current balanace 0.00010% and unemployment rate is mitigated 0.00233%.