• 한국컴퓨터정보학회논문지
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• 제24권6호
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• pp.89-97
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• 2019

This study proposes a method for investigating current patents related to information communication technology and smart mobility to provide insights into future technology trends. The method is based on text mining clustering analysis. The method consists of two stages, which are data preparation and clustering analysis, respectively. In the first stage, tokenizing, filtering, stemming, and feature selection are implemented to transform the data into a usable format (structured data) and to extract useful information for the next stage. In the second stage, the structured data is partitioned into groups. The K-medoids algorithm is selected over the K-means algorithm for this analysis owing to its advantages in dealing with noise and outliers. The results of the analysis indicate that most current patents focus mainly on smart connectivity and smart guide systems, which play a major role in the development of smart mobility.

• 산업경영시스템학회지
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• 제36권1호
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• pp.24-35
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• 2013

A cell formation approach based on cluster analysis is developed for the configuration of manufacturing cells. Cell formation, which is to group machines and parts into machine cells and the associated part families, is implemented to add the flexibility and efficiency to manufacturing systems. In order to develop an efficient clustering procedure, this paper proposes a cluster analysis-based approach developed by incorporating and modifying two cluster analysis methods, a hierarchical clustering and a non-hierarchical clustering method. The objective of the proposed approach is to minimize intercellular movements and maximize the machine utilization within clusters. The proposed approach is tested on the cell formation problems and is compared with other well-known methodologies available in the literature. The result shows that the proposed approach is efficient enough to yield a good quality solution no matter what the difficulty of data sets is, ill or well-structured.

• Journal of the Korean Data and Information Science Society
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• 제14권3호
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• pp.535-543
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• 2003

Cluster analysis has been widely used in many applications, such as pattern analysis or recognition, data analysis, image processing, market research on on-line or off-line and so on. Clustering can identify dense and sparse regions among data attributes or object attributes. But it requires many hours to get clusters that we want, because clustering is more primitive, explorative and we make many data an object of cluster analysis. In this paper we propose a new method of clustering using sample based on grid. It is more fast than any traditional clustering method and maintains its accuracy.

• 산업경영시스템학회지
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• 제25권6호
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• pp.36-41
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• 2002

We consider the Fuzzy clustering which is devised for partitioning a set of objects into a certain number of groups by assigning the membership probabilities to each object. The researches carried out in this field before show that the Fuzzy clustering concept is involved so much that for a certain set of data, the main purpose of the clustering cannot be attained as desired. Thus we propose a new objective function, named as Fuzzy-Entroppy Function in order to satisfy the main motivation of the clustering which is classifying the data clearly. Also we suggest Mean Field Annealing Algorithm as an optimization algorithm rather than the ISODATA used traditionally in this field since the objective function is changed. we show the Mean Field Annealing Algorithm works pretty well not only for the new objective function but also for the classical Fuzzy objective function by indicating that the local minimum problem resulted from the ISODATA can be improved.

• 산업경영시스템학회지
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• 제43권2호
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• pp.25-32
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• 2020

Data on patent and scientific paper is considered as a useful information source for analyzing technological information and has been widely utilized. Technology big data is analyzed in various ways to identify the latest technological trends and predict future promising technologies. Clustering is one of the ways to discover new features by creating groups from technology big data. Patent includes refined bibliographic information such as patent classification code whereas scientific paper does not have appropriate bibliographic information for clustering. This research proposes a new approach for clustering data of scientific paper by utilizing reference titles in each scientific paper. In this approach, the reference titles are considered as textual information because each reference consists of the title of the paper that represents the core content of the paper. We collected the scientific paper data, extracted the title of the reference, and conducted clustering by measuring the text-based similarity. The results from the proposed approach are compared with the results using existing methodologies that one is the approach utilizing textual information from titles and abstracts and the other one is a citation-based approach. The suggested approach in this paper shows statistically significant difference compared to the existing approaches and it shows better clustering performance. The proposed approach will be considered as a useful method for clustering scientific papers.

• 대한산업공학회지
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• 제38권1호
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• pp.67-73
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• 2012

Identification of meaningful patterns and trends in large volumes of text data is an important task in various research areas. In the present study we crawled the keywords from the abstracts in IIE Transactions, one of the representative journals in the field of Industrial Engineering from 1969 to 2011. We applied low-dimensional embedding method, clustering analysis, association rule, and social network analysis to find meaningful associative patterns of key words frequently appeared in the paper.

• 한국산학기술학회논문지
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• 제19권2호
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• pp.370-379
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• 2018

우리나라는 중이온 가속기 건설을 중심으로 과학지식이 사업화로 연결될 수 있도록 하는 지리적 공간으로서의 국제과학비즈니스벨트에 관한 계획을 2009년에 확정하였다. 과학기반 클러스터의 형성 단계에서 국제과학비즈니스벨트의 각 클러스터의 우선 유치업종의 선택은 클러스터의 성격과 발전에 많은 영향을 미칠 수 있다는 점에서 매우 중요하다. 본 연구는 정부에서 제시한 국제과학비즈니스벨트의 과학기반 혁신클러스터의 조성을 위해 유치해야 할 핵심 업종들을 제시하고자 한다. 산업별로 클러스터 형성과정이 상이할 뿐만 아니라, 산업 내 특정 업종이 클러스터의 성장과정에 미치는 영향력이 다르기 때문에 혁신생태계 조성을 위한 앵커 섹터를 파악하는 것이 매우 중요하다. 국제과학비즈니스벨트 내 4개 클러스터의 형성 및 성장을 위한 기업을 분석하기 위해 본 연구는 Swann & Prevezer의 산업 클러스터링(industrial clustering) 모델을 활용하여 분석하였다. 본 연구에서는 기업 관련 자료의 경우 2014년의 제조업 및 서비스업 대상 한국기업혁신조사(ICT 클러스터), 2014년 국내 바이오산업 실태조사(바이오헬스케어 클러스터), 2015 국내 나노융합산업 실태조사(첨단산업 클러스터)에 관한 최신자료를 이용하였다. ICT, 바이오헬스케어, 나노 등 3개 산업군에 대한 클러스터링 분석을 수행한 결과 각 산업군에는 다른 여러 섹터에 속하는 기업들의 지역 내 진입을 유발하는 중심적 역할을 하는 섹터들이 있는 것으로 나타났는데, ICT 산업의 경우 정보통신서비스 섹터, 바이오헬스케어 산업의 경우 바이오공정/기기 섹터, 나노 산업군의 경우 나노전자 섹터가 각각 중심적 역할을 하는 것으로 분석되었다. 분석 결과를 바탕으로 본 연구는 국제과학비즈니스벨트 클러스터를 육성하기 위한 기업 유치 전략과 정책에 대한 시사점을 제시하였다.

• 대한산업공학회지
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• 제27권2호
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• pp.214-225
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• 2001

This research deals with an off-line learning method targeted for systematically constructing negotiation strategies in automated electronic commerce. Single-issue negotiation is assumed. Variants of competitive learning and hierarchical clustering method are devised and applied to extracting negotiation strategies, given historical negotiation data set and tactics. Our research is motivated by the following fact: evidence from both theoretical analysis and observations of human interaction shows that if decision makers have prior knowledge on the behaviors of opponents from negotiation, the overall payoff would increase. Simulation-based experiments convinced us that the proposed method is more effective than human negotiation in terms of the ratio of negotiation settlement and resulting payoff.

• 산업경영시스템학회지
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• 제41권2호
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• pp.1-8
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• 2018

K-means algorithm is one of the most popular and widely used clustering method because it is easy to implement and very efficient. However, this method has the limitation to be used with fixed number of clusters because of only considering the intra-cluster distance to evaluate the data clustering solutions. Silhouette is useful and stable valid index to decide the data clustering solution with number of clusters to consider the intra and inter cluster distance for unsupervised data. However, this valid index has high computational burden because of considering quality measure for each data object. The objective of this paper is to propose the fast and simple speed-up method to overcome this limitation to use silhouette for the effective large-scale data clustering. In the first step, the proposed method calculates and saves the distance for each data once. In the second step, this distance matrix is used to calculate the relative distance rate ($V_j$) of each data j and this rate is used to choose the suitable number of clusters without much computation time. In the third step, the proposed efficient heuristic algorithm (Group search optimization, GSO, in this paper) can search the global optimum with saving computational capacity with good initial solutions using $V_j$ probabilistically for the data clustering. The performance of our proposed method is validated to save significantly computation time against the original silhouette only using Ruspini, Iris, Wine and Breast cancer in UCI machine learning repository datasets by experiment and analysis. Especially, the performance of our proposed method is much better than previous method for the larger size of data.

• 산업경영시스템학회지
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• 제46권4호
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• pp.63-73
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• 2023

This study explores modern portfolio theory by integrating the Black-Litterman portfolio with time-series clustering, specificially emphasizing K-shape clustering methodology. K-shape clustering enables grouping time-series data effectively, enhancing the ability to plan and manage investments in stock markets when combined with the Black-Litterman portfolio. Based on the patterns of stock markets, the objective is to understand the relationship between past market data and planning future investment strategies through backtesting. Additionally, by examining diverse learning and investment periods, it is identified optimal strategies to boost portfolio returns while efficiently managing associated risks. For comparative analysis, traditional Markowitz portfolio is also assessed in conjunction with clustering techniques utilizing K-Means and K-Means with Dynamic Time Warping. It is suggested that the combination of K-shape and the Black-Litterman model significantly enhances portfolio optimization in the stock market, providing valuable insights for making stable portfolio investment decisions. The achieved sharpe ratio of 0.722 indicates a significantly higher performance when compared to other benchmarks, underlining the effectiveness of the K-shape and Black-Litterman integration in portfolio optimization.