• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.3
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• pp.89-98
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• 2014

Platform-based product family design is recognized as an effective method to satisfy the mass customization which is a current market trend. In order to design platform-based product family successfully, it is the key work to define a good product platform, which is to identify the common modules that will be shared among the product family. In this paper the clustering analysis using dendrogram is proposed to capture the common modules of the platform. The clustering variables regarding both marketing and engineering sides are derived from the view point of top-down product development. A case study of a cordless drill/drive product family is presented to illustrate the feasibility and validity of the overall procedure developed in this research.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.2
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• pp.311-322
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• 1998

Modular BOMs are typically used in TWO-Level Master Production Schedule. To solve the problems of Modular BOM generation and efficient DB construction, we proposed Value Clustering Method. Based upon Where-Used matrix of products and components, VCM is the method to find out module by generating product family group value, product value, and component value. We also proposed method to find out information about Modules, algorithms to find out Modules that show Alternative Usage Pattern, and method to find out Modules used in a given product. We also compared the DB creation method by Value Clustering Method and by conventional method. We compared the size of DB in both methods. We mathematically proved that the proposed method is doing better as the size and complexity of product family gets larger and more complicated.

• The KIPS Transactions:PartD
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• v.8D no.5
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• pp.573-580
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• 2001

The large amounts of high dimensional data contains a significant amount of noises by it own sparsity, which adds difficulties in high dimensional clustering. The CLIP is developed as a clustering algorithm to support characteristics of the high dimensional data. The CLIP is based on the incremental one dimensional projection on each axis and find product sets of the dimensional clusters. These product sets contain not only all high dimensional clusters but also they may contain noises. In this paper, we propose extended CLIP algorithm which refines the product sets that contain cluster. We remove high dimensional noises by applying two dimensional projections iteratively on the already found product sets by CLIP. To evaluate the performance of extended algorithm, we demonstrate its effectiveness through a series of experiments on synthetic data sets.

• Korean Management Science Review
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• v.19 no.1
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• pp.189-196
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• 2002

Data clustering is often one of the first steps in data mining analysis. It Identifies groups of related objects that can be used as a starling point for exploring further relationships. This technique supports the development of population segmentation models, such as demographic-based customer segmentation. This paper Purpose to present the development of two phase hierarchical clustering algorithm for group formation. Applications of the algorithm for product-customer group formation in customer relationahip management are also discussed. As a result of computer simulations, suggested algorithm outperforms single link method and k-means clustering.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.625-631
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• 2002

A module design aims to develop product architecture that consists physically detachable units such as module. To develop the system of module design, this paper suggests the methodology of part grouping, evaluation of modularization of a product for improving modularization. To determine modules, module concept is proposed to satisfy the objectives of a modular design. Therefore, there are functional, structural, and process modularizations in a modular concept. Module grouping can be accomplished by using an optimization model that maximizes the sum of the weighting. The present study proposes the p-median model and the direct clustering technique. The optimal clustering solution can be obtained by comparing two clustering techniques. To find the best solution among part groups, evaluation of modularization is performed based on the concept of module design. For the evaluation of modularization, evaluation criteria of modularization are used in the matrix table.

• Journal of information and communication convergence engineering
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• v.6 no.2
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• pp.182-186
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• 2008

RFID systems generate huge amount of data quickly. The data are associated with the locations and the timestamps and the containment relationships. It is requires to assure efficient queries and updates for product tracking and monitoring. We propose a clustering technique for fast query processing. Our study presents the state charts of temporal event flow and proposes the dependence trees with data association and uses them to cluster the linked events. Our experimental evaluation show the power of proposing clustering technique based on dependence tree.

• The Journal of Society for e-Business Studies
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• v.19 no.2
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• pp.95-108
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• 2014

Opinion Mining is one of the application domains of text mining which extracting opinions from documents, and much researches are currently underway. Most of related researches focused on the sentiment classification which classifies the documents into positive/negative opinions. However, there is a little interest in extracting the features characterizing the individual product. In this paper, we propose the technique classifying the opinion documents according to the product features, and selecting the those features characterizing each product. In the proposed method, we utilize the document clustering technique and develope a new algorithm for evaluating the similarity between documents. In addition, through experiments, we prove the usefulness of proposed method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.263-272
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• 2016

Modular product design has contributed to flexible product modification and development, production lead time reduction, and increasing product diversity. Modular product design aims to develop a product architecture that is composed of detachable modules. These modules are constructed by maximizing the similarity of components based on physical and functional interaction analysis among components. Accordingly, a systematic procedure for clustering the components, which is a main activity in modular product design, is proposed in this paper. The first phase in this procedure is to build a component-to-component correlation matrix by analyzing physical and functional interaction relations among the components. In the second phase, network flow modeling is applied to find clusters of components, maximizing their correlations. In the last phase, a network flow model formulated with linear programming is solved to find the clusters and to make them modular. Finally, the proposed procedure in this research and its application are illustrated with an example of modularization for a vacuum cleaner.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.118-126
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• 2003

In data mining, having access to large amount of data sets for the purpose of predictive data does not guarantee good method, even where the size of Real data is Mobile commerce unlimited. In addition to searching expected Goods objects for Users, it becomes necessary to develop a recommendation service based on XML. In this paper, we design the optimized XML Recommender product data. Efficient XML data preprocessing is required, include of formatting, structural, and attribute representation with dependent on User Profile Information. Our goal is to find a relationship among user interested products from E-Commerce and M-Commerce to XDB. Firstly, analyzing user profiles information. In the result creating clusters with analyzed user profile such as with set of sex, age, job. Secondly, it is clustering XML data which are associative products classify from user profile in shopping mall. Thirdly, after composing categories and goods data in which associative objects exist from the first clustering, it represent categories and goods in shopping mall and optimized clustering XML data which are personalized products. The proposed personalized user profile clustering method has been designed and simulated to demonstrate it's efficient.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.1
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• pp.1-18
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• 2023

Similar software is often developed with the Clone-And-Own (CAO) approach that copies and modifies existing artifacts. The CAO approach is considered as a bad practice because it makes maintenance difficult as the number of cloned products increases. Software product line engineering is a methodology that can solve the issue of the CAO approach by developing a product family through systematic reuse. Migrating product families that have been developed with the CAO approach to the product line engineering begins with finding, integrating, and building them as reusable assets. However, cloning occurs at various levels from directories to code lines, and their structures can be changed. This makes it difficult to build product line code base simply by finding clones. Successful migration thus requires unifying the source code's file path, class name, and method signature. This paper proposes a clustering method that identifies a set of similar codes scattered across product variants and some of their method full paths are different, so path unification is necessary. In order to show the effectiveness of the proposed method, we conducted an experiment using the Apo Games product line, which has evolved with the CAO approach. As a result, the average precision of clustering performed without preprocessing was 0.91 and the number of identified common clusters was 0, whereas our method showed 0.98 and 15 respectively.