• Journal of Information Technology Applications and Management
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• 제22권3호
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• pp.83-103
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• 2015

The rapid development of internet technologies and social media over the last few years has generated a huge amount of unstructured text data, which contains a great deal of valuable information and issues. Therefore, text mining-extracting meaningful information from unstructured text data-has gained attention from many researchers in various fields. Topic analysis is a text mining application that is used to determine the main issues in a large volume of text documents. However, it is difficult to identify related issues or meaningful insights as the number of issues derived through topic analysis is too large. Furthermore, traditional issue-clustering methods can only be performed based on the co-occurrence frequency of issue keywords in many documents. Therefore, an association between issues that have a low co-occurrence frequency cannot be recognized using traditional issue-clustering methods, even if those issues are strongly related in other perspectives. Therefore, in this research, a methodology to reorganize social issues from a research and development (R&D) perspective using social network analysis is proposed. Using an R&D perspective lexicon, issues that consistently share the same R&D keywords can be further identified through social network analysis. In this study, the R&D keywords that are associated with a particular issue imply the key technology elements that are needed to solve a particular issue. Issue clustering can then be performed based on the analysis results. Furthermore, the relationship between issues that share the same R&D keywords can be reorganized more systematically, by grouping them into clusters according to the R&D perspective lexicon. We expect that our methodology will contribute to establishing efficient R&D investment policies at the national level by enhancing the reusability of R&D knowledge, based on issue clustering using the R&D perspective lexicon. In addition, business companies could also utilize the results by aligning the R&D with their business strategy plans, to help companies develop innovative products and new technologies that sustain innovative business models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권6호
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• pp.2619-2643
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• 2018

In this paper, a new clustering algorithm based on the connected region generation (CRG-clustering) is proposed. It is an effective and robust approach to clustering on the basis of the connectivity of the points and their neighbors. In the new algorithm, a connected region generating (CRG) algorithm is developed to obtain the connected regions and an isolated point set. Each connected region corresponds to a homogeneous cluster and this ensures the separability of an arbitrary data set theoretically. Then, a region expansion strategy and a consensus criterion are used to deal with the points in the isolated point set. Experimental results on the synthetic datasets and the real world datasets show that the proposed algorithm has high performance and is insensitive to noise.

• 통상정보연구
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• 제7권4호
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• pp.245-268
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• 2005

The purpose of this paper is to acquire competitiveness faced with a global business so that Korea and China make them put ICT into practice through industrial policy of regional innovation clustering. In the Chapter 2, overall review of industrial spaces theory and the environment in Global-business is conducted. In the Chapter 3, current main economic issue and West Great Development of China are viewed. Chapter 4 proposes models and strategies for the target of regional innovation clustering and phasing in development. The results of this study is that both country should do more long-term cooperation and collecting intensive knowledge for the property of region and preparatory research of regional innovation clustering than do reckless investment.

• 전기학회논문지
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• 제58권6호
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• pp.1230-1237
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• 2009

In this paper, we develop a design methodology of Granular-based Radial Basis Function Neural Networks(GRBFNN) by context-based clustering. In contrast with the plethora of existing approaches, here we promote a development strategy in which a topology of the network is predominantly based upon a collection of information granules formed on a basis of available experimental data. The output space is granulated making use of the K-Means clustering while the input space is clustered with the aid of a so-called context-based fuzzy clustering. The number of information granules produced for each context is adjusted so that we satisfy a certain reconstructability criterion that helps us minimize an error between the original data and the ones resulting from their reconstruction involving prototypes of the clusters and the corresponding membership values. In contrast to "standard" Radial Basis Function neural networks, the output neuron of the network exhibits a certain functional nature as its connections are realized as local linear whose location is determined by the values of the context and the prototypes in the input space. The other parameters of these local functions are subject to further parametric optimization. Numeric examples involve some low dimensional synthetic data and selected data coming from the Machine Learning repository.

• Earthquakes and Structures
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• 제23권6호
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• pp.549-580
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• 2022

This paper assesses the influences of modeling assumptions and uncertainties on the performance of the non-linear finite element (FE) model updating procedure and model clustering method. The results of a shaking table test on a four-story steel moment-resisting frame are employed for both calibrations and clustering of the FE models. In the first part, simple to detailed non-linear FE models of the test frame is calibrated to minimize the difference between the various data features of the models and the structure. To investigate the effect of the specified data feature, four of which include the acceleration, displacement, hysteretic energy, and instantaneous features of responses, have been considered. In the last part of the work, a model-based clustering approach to group models of a four-story frame with similar behavior is introduced to detect abnormal ones. The approach is a composition of property derivation, outlier removal based on k-Nearest neighbors, and a K-means clustering approach using specified data features. The clustering results showed correlations among similar models. Moreover, it also helped to detect the best strategy for modeling different structural components.

• 한국안광학회지
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• 제20권4호
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• pp.405-414
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• 2015

목적: 안경원 고객에 대한 군집분석을 통하여 고객의 세분시장 특성을 분석하고, 이를 안경원 마케팅 전략에 유용한 지표로 삼을 수 있도록 하고자 한다. 방법: 2015년 3월 10일부터 3월 31일 사이에 서울 및 경기북부지역의 안경원을 방문한 고객을 대상으로 설문조사를 실시하였으며, SPSS v.10.0 통계 패키지 프로그램을 활용하여 분석하였다. 분석방법은 빈도분석, 가치관 변수에 대한 요인분석, 시장세분화를 위한 군집분석, 교차분석을 실시하였다. 결과: 가치관에 근거하여 시장을 세분화하였다. 마케팅 전략을 수립할 때 "중도가치 지향 집단", "고도가치 지향 집단", "고도가치 지향 비종교 집단"의 3가지 군집으로 고객을 분류하여 전략을 수립하는 것이 좋은 것으로 나타났다. 누진굴절력렌즈의 마케팅 전략은 "중도가치 지향 집단"에서 자영업자를 중심으로 하는 전략이 가장 중요한 것으로 나타났다. 결론: 군집분석을 통하여 시장을 세분화한 결과 3가지 군집으로 분류되었으며, 누진굴절력렌즈의 가장 중요한 고객은 "중도가치 지향 집단"에서 41세 이상의 자영업자임을 알 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.514-537
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• 2020

Wireless sensor network (WSN) is a distributed network composed of many sensory nodes. It is precisely due to the clustering unevenness and cluster head election randomness that the energy consumption of WSN is excessive. Therefore, a many-objective optimization WSN energy balance model is proposed for the first time in the clustering stage of LEACH protocol. The four objective is considered that the cluster distance, the sink node distance, the overall energy consumption of the network and the network energy consumption balance to select the cluster head, which to better balance the energy consumption of the WSN network and extend the network lifetime. A many-objective optimization algorithm to optimize the model (LEACH-ABF) is designed, which combines adaptive balanced function strategy with penalty-based boundary selection intersection strategy to optimize the clustering method of LEACH. The experimental results show that LEACH-ABF can balance network energy consumption effectively and extend the network lifetime when compared with other algorithms.

• 인터넷정보학회논문지
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• 제10권3호
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• pp.61-69
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• 2009

무선 센서 네트워크에서 클러스터 기반의 계층적 라우팅 프로토콜들은 모든 노드들의 수명을 균등하게 유지하여, 센서 네트워크의 수명을 최대로 연장하는 것을 목표로 하고 있다. 본 논문에서는 임계값 기반의 클러스터 헤드 교체 전략을 보인다. 이라우팅 프로토콜은 센서 노드가 클러스터 헤드의 역할을 수행하지 않으면서 최대한 오랜 시간 동안 네트워크에 참여하기 위해 필요한 에너지의 양을 임계값으로 정의하고, 한 번 클러스터 헤드로 선출된 노드는 이 값에 도달할 때 까지 클러스터 헤드의 역할을 유지하여 빈번한 헤드 교체를 방지하도록 함으로써 전체 네트워크의 에너지 효율은 물론 네트워크의 균형이 최대한 유지되도록 한다. 또한 시뮬레이션을 통해 대표적 계층적 라우팅 프로토콜인 LEACH와 네트워크의 균형 및 에너지 소모 정도를 비교하여 본 논문이 제시하는 알고리즘의 효용성을 입증한다.

• Journal of Electrical Engineering and Technology
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• 제4권3호
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• pp.330-336
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• 2009

This paper proposes an on-line voltage management and control solution for a distribution system which can improve the efficiency and accuracy of existing off-line work by collecting customer voltage on-line as well as the voltage compensation capability of the existing ULTC (Under Load Tap Changer) operation and control strategy by controlling the ULTC tap based on pattern clustering and recognition. The proposed solution consists of an ADVMD (Advanced Digital Voltage Management Device), a VMS (Voltage Management Solution) and an OLDUC (On-Line Digital ULTC Controller). An on-line voltage management emulator based on multi-thread programming and the shared memory method is developed to emulate on-line voltage management and digital ULTC control methodology based on the on-line collection of the customer's voltage. In addition, using this emulator, the effectiveness of the proposed pattern clustering and recognition based ULTC control strategy is proven for the worst voltage environments for three days.

• Journal of Information Technology Applications and Management
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• 제19권2호
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• pp.97-116
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• 2012

Since 2008 when IPTV service entered the broadcasting market, the competition among interactive broadcasting media has been growing more and more fierce. To make a market strategy under the harsh competition, this study tried to make an IPTV customer segmentation based on the characteristics of interactive broadcasting media. From previous literature, this study drew five characteristics of interactive broadcasting media : ease of use, two-way communications, active control, variety of content, and economic efficiency. Two-step clustering based on these characteristics identified four customer segments. There were statistically significant differences in the five characteristics among the customer segments. This study profiled the customer segments and proposed competitive strategies for each customer segment.