• Journal of KIISE
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• v.43 no.9
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• pp.983-997
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• 2016

Many available online product reviews for any given product makes it difficult for a consumer to locate the helpful reviews. The purpose of this study was to investigate automatic helpfulness evaluation of online product reviews according to review information types based on the target of information. The underlying assumption was that consumers find reviews containing specific information related to the product itself or the reliability of reviewers more helpful than peripheral information, such as shipping or customer service. Therefore, each sentence was categorized by given information types, which reduced the semantic space of review sentences. Subsequently, we extracted specific information from sentences by using a topic-based representation of the sentences and a clustering algorithm. Review ranking experiments indicated more effective results than other comparable approaches.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.7
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• pp.72-84
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• 2001

This paper proposes a design methodology for customer, assembly and disassembly considering recycling. The disign process starts with the identification of customer needs, which are in turn converted into functional requirements. The concepts of Design for Customer(DFC), Design for Assembly(DFA), Design for Variety(DFV) and Design for Disassembly(DFD) are considered in the product design phases in order to decreas production variety and mass customization. And, a new module generation approach is developed for rearranging and clustering parts and subassemblies for disassembly and recycling. Based on the result of the module generation, a new configuration methodology is suggested to minimize the disassembly time or number of disassembly operations for recycling.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.433-450
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• 2002

The concept of VDD(Virtual Design & Development) based on high performance computing technology is introduced. The whole process of design and development could be carried out in cyber space through VDD technology in which high-precision simulation technology is combined with virtual reality technology. Therefore, time for design and development of a new product can be shorten and quality of product can be also improved. However, efficient simulation technology must be developed and appropriate supercomputing system must be available to realize VDD technology. In this work, several technologies for obtaining cost-effective supercomputing systems for VDD technology are introduced and investigated. The detail informations about optimization technology for PC(Personal Computer) computing, clustering technology, Internet Supercomputing technology, and Piggyback technology are provided and numerical results are also presented.

• The Transactions of the Korea Information Processing Society
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• v.7 no.10
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• pp.3098-3104
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• 2000

This paper defiens a perceptron-like self-organizing map(PSOM) and show that PSOM is equivalent to Kohonen's self-organizing map(SOM) if target values of output neurons of PSOM are selected properly. This fact imphes that PSOM is a generalized SOM algorithm. This paper also show that if clustering is restricted to vector sets distributed on hypersphere with unit radius, SOM and dot-product SOM(DOSM) are equivalent algorithms. Therefore we conclude that DSOM is a special case of SOM, which in turn a special, case of PSOM.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.3
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• pp.339-348
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• 2007

Since the 1980s, the marketing paradigm has rapidly changed from product-driven marketing to customer-driven marketing. Recently, due to an increase in the amount of information, customer-differentiation strategies have been emphasized more than product-differentiation strategies. This paper suggests a methodology for new customer segmentation and purchase forecasting using changes in customer behavior. This methodology includes a segmentation method for new customers using existing customer's characteristics and a purchase-forecasting system using the purchase-behavior patterns of existing customers. The proposed methodology not only provides differential services from a segmentation system but also recommends differential items from the purchase forecasting system for new and existing customers.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.37-51
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• 2009

One of the critical issues in the management of manufacturing companies is the efficient process of planning and operating service resources such as human, parts, and facilities, and it begins with the accurate service demand forecasting. In this research, service and sales data from the LCD monitor manufacturer is considered for an empirical study on Product Life Cycle (PLC) based service demand forecasting. The proposed PLC forecasting approach consists of four steps : understanding the basic statistics of data, clustering models using a self-organizing map, developing respective forecasting models for each segment, comparing the accuracy performance. Empirical experiments show that the PLC approach outperformed the traditional approaches in terms of root mean square error and mean absolute percentage error.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.1
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• pp.33-43
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• 2013

The research on data mining that can manage a large amount of information efficiently has grown with the drastic increment of information. Privacy-preserving data mining can protect the privacy of data owners. There are several privacy-preserving association rule, clustering and classification protocols. A privacy-preserving association rule protocol is used to find association rules among data, which is often used for marketing. In this paper, we propose a privacy-preserving k-bits inner product protocol based on Shamir's secret sharing.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.3
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• pp.291-298
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• 2014

Demand forecasting plays a key role in overall business activities such as production planning, distribution management, and inventory management. Especially, for a fast-changing environment of the clothing industry, logical forecasting techniques are required. In this study, we propose a procedure to predict product life cycle using data mining algorithms. The proposed procedure involves three steps : extracting key variables from profiles, clustering, and classification. The effectiveness and applicability of the proposed procedure were demonstrated through a real data from a leading clothing company in Korea.

• IE interfaces
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• v.15 no.2
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• pp.114-125
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• 2002

The objective of this paper is to describe a systematic accident scenario analysis method(SASA) adept at creating accident scenarios for the design of safer products. This approach was inspired by the Quality Function Deployment(QFD) method, which is conventionally used in quality management. In this study, the QFD provides a formal and systematic scheme to devise accident scenarios while maintaining objectivity. SASA consists of three key stages to be broken down into a series of consecutive steps:(1) developing an accident analysis tableau,(2) devising the accident scenarios using the accident analysis tableau,(3) performing a feasibility test, a clustering process and a patterning process, and finally(4) performing quantitative evaluation of each accident scenario. The SASA was applied to a case study of child safety seats. The accident analysis tableau devised 2828(maximum) accident scenarios from all possible relationships between the hazard factors and situation characteristics. Among them, 270 scenarios were devised through the feasibility test and the clustering process. The patterning process reduced them to 29 patterns representative of all accident scenarios. Based on an intensive analysis of the accident patterns, design guidelines for a safer child safety seat were recommended. The implications of the study on the child safety seat case were then discussed.

• Journal of Distribution Science
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• v.18 no.2
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• pp.69-77
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• 2020

Purpose - This paper is aimed to analyze the effects of the mandatory closing regulation targeting large retailers, which has been implemented since 2012 to protect small retailers. We examine the changes in consumers' choice of retailers and their purchasing patterns of agri-food following the implementation of such regulation. Research design, data, and methodology - Household spending patterns were identified through the historical data of household food purchase, consumer panel provided by the Rural Development Administration. Clustering was employed to determine the household spending patterns. Moreover, the different household spending patterns before and after the regulation were comparatively studied. The patterns of consumers' choice of retail stores and shopping baskets by the type of retailers, derived from the respective datasets before and after the regulation, were compared to analyze the effects of the regulation. Results -After the regulation, some consumers who used to shop at large retailers before the regulation changed their shopping places to small retailers. However, the product categories that consumers had mainly purchased before the regulation were rarely changed even after the regulation. Conclusions - Although the regulation helped migrate some of the consumers to small retailers, the regulation seemed to have failed to stimulate consumers to purchase the goods, normally bought at large retailers, from traditional markets. In other words, traditional markets are not effective substitutes for regulation-affected retailers.