• Journal of the Korean Society of Safety
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• v.11 no.1
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• pp.39-45
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• 1996

In this study, clustering of black BADUK stones' image were performed to recognize the individual stone from its closely spaced and partially occluded Image. And the clustering perfomance was compared between the classical methods and fuzzy C-means method. As a result, 2 BADUK stones' image was segmented precisely in every methods, but more than 3 stones the segmentation was depended on its shape. Fuzzy C-means method could be segmented correctly to 4 stones regardless of its shape, and It could be applied to the unknown number of clusters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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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.

• 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.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.2
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• pp.85-93
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• 2016

The existence of correlation characteristics brings significant potential advantages to the development of efficient routing protocols in wireless sensor networks. This research proposes a new simple method of clustering sensor nodes into correlation groups in multiple-correlation areas. At first, the evaluation of joint entropy for multiple-sensed data is considered. Based on the evaluation, the definition of correlation region, based on entropy theory, is proposed. Following that, a correlation clustering scheme with less computation is developed. The results are validated with a real data set.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.572-579
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• 2003

With the expansion of e-commerce, E-marketplace has become one of the most discussed topics in recent years. Limited theoretical works, however, have been done to optimize the practical use of e-marketplace systems. Other potential issues aside, this research has focused on this problem: 'the participants waste too much time, effort and cost to find out their best partner in B2B marketplace.' To solve this problem, this paper proposes a system which provides the user-company with the automated and customized brokering service. The system proposed in this paper assesses the weight on the priorities of a user-company, runs the two-stage clustering algorithm with self-organizing map and K-means clustering technique. Subsequently, the system shows the clustering result and user guide-line. This system enables B2B marketplace to have more efficiency on transaction with smaller pool of partners to be searched.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.7B
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• pp.488-501
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• 2008

Convergence of various wireless systems can be cost effectively achieved through enhancement of existing technology. The emergence of Wireless Mesh Network (WMN) entails the interoperability and interconnection of various wireless technologies in one single system. Furthermore, WMN can be implemented with multi-radio and multi-channel enhancement. A multi-radio, multi-channel wireless mesh network could greatly improve certain networking performance metrics. In this research, two approaches namely, clustering and topology control mechanisms are integrated with multi-radio multi-channel wireless mesh network. A Clustering and Topology Control Algorithm (CTCA)is presented that would prolong network lifetime of the client nodes and maintain connectivity of the routers.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.6
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• pp.403-406
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• 2015

In this paper, we propose a supervoxel clustering-based staircase extraction algorithm to obtain poses and dimensions of staircases from a point cloud. In order to effectively reduce the candidate points and accelerate supervoxel clustering, large planes in the scene, such as walls, floors, and ceilings, are eliminated while scanning the environment. Next, staircase candidates with small planes are initially estimated using supervoxel clustering. Then, parameter values for the staircases are refined, and higher staircases that remain undetected due to occlusion are predicted and generated virtually. Experimental results show that staircases are detected accurately and predicted successfully.

• KIEE International Transactions on Electrophysics and Applications
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• v.5C no.3
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• pp.138-142
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• 2005

In this paper, we compared recognition rates between NN(neural networks) and clustering method as a scheme of off-line PD(partial discharge) diagnosis which occurs at the stator coil of traction motor. To acquire PD data, three defective models are made. PD data for classification were acquired from PD detector. And then statistical distributions are calculated to classify model discharge sources. These statistical distributions were applied as input data of two classification tools, BP(Back propagation algorithm) and ANFIS(adaptive network based fuzzy inference system) pre-processed FCM(fuzzy c-means) clustering method. So, classification rate of BP were somewhat higher than ANFIS. But other items of ANFIS were better than BP; learning time, parameter number, simplicity of algorithm.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.1
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• pp.90-99
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• 2003

Object clustering, which makes classification for a set of objects into a number of groups such that objects included in a group have similar characteristic and objects in different groups have dissimilar characteristic each other, has been exploited in diverse area such as information retrieval, data mining, group technology, etc. In this study, an object-clustering problem with similarity coefficients between objects is considered. At first, an evaluation function for the optimization problem is defined. Then, a genetic algorithm and local optimization technique based on heuristic method are proposed and used in order to obtain near optimal solutions. Solutions from the genetic algorithm are improved by local optimization techniques based on object relocation and cluster merging. Throughout extensive experiments, the validity and effectiveness of the proposed algorithms are tested.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.601-604
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• 2000

In this paper, we compare the classification performance of both data fusion and clustering algorithms (Data Bagging, Variable Selection Bagging, Parameter Combining, Clustering) to logistic regression in consideration of various characteristics of input data. Four factors used to simulate the logistic model are (1) correlation among input variables (2) variance of observation (3) training data size and (4) input-output function. Since the relationship between input & output is not typically known, we use Taguchi design to improve the practicality of our study results by letting it as a noise factor. Experimental study results indicate the following: Clustering based logistic regression turns out to provide the highest classification accuracy when input variables are weakly correlated and the variance of data is high. When there is high correlation among input variables, variable bagging performs better than logistic regression. When there is strong correlation among input variables and high variance between observations, bagging appears to be marginally better than logistic regression but was not significant.