• The Research Journal of the Costume Culture
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• v.15 no.2 s.67
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• pp.252-264
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• 2007

The purpose of this study was to compare two analytical methods classifying foot shape. The methods compared were cluster analysis method and foot index analysis method. This study defined the women's foot shape by these methods. 39 foot measurements which were automatically collected using the three dimensional foot scanner were analyzed. 203 Korean women in age 20s were participated in the anthropometric survey. Their foot shapes were classified into 5 foot types by cluster analysis: short & slim shape, flat shape, short & slender shape with slightly distorted toe, long and big shape, and short & wide shape. The foot measurements were also analyzed by the ratio of foot width and length. Five foot types that were classified by cluster analysis and three foot types that were classified by the foot index were compared. The comparison shows that cluster analysis precisely defined foot shapes. It was suggested that made-to-measure shoes making industry may adopt the foot shape analysis method utilizing cluster analysis.

• The Journal of the Korea Contents Association
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• v.9 no.9
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• pp.80-89
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• 2009

A fast and efficient search algorithm to determine an optimal number of clusters in clustering algorithms is presented. The method is based on cluster validity index which is a measure for clustering optimality. As the clustering procedure progresses and reaches an optimal cluster configuration, the cluster validity index is expected to be minimized or maximized. In this Paper, a fast non-exhaustive search method for finding the optimal number of clusters is designed and shown to work well in clustering. The proposed algorithm is implemented with the k-mean++ algorithm as underlying clustering techniques using CB and PBM as a cluster validity index. Experimental results show that the proposed method provides the computation time efficiency without loss of accuracy on several artificial and real-life data sets.

• Communications for Statistical Applications and Methods
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• v.25 no.4
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• pp.373-383
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• 2018

The spatial scan statistic is a widely used method to detect spatial clusters. The method imposes a large number of scanning windows with pre-defined shapes and varying sizes on the entire study region. The likelihood ratio test statistic comparing inside versus outside each window is then calculated and the window with the maximum value of test statistic becomes the most likely cluster. The results of cluster detection respond sensitively to the shape and the maximum size of scanning windows. The shape of scanning window has been extensively studied; however, there has been relatively little attention on the maximum scanning window size (MSWS) or maximum reported cluster size (MRCS). The Gini coefficient has recently been proposed by Han et al. (International Journal of Health Geographics, 15, 27, 2016) as a powerful tool to determine the optimal value of MRCS for the Poisson-based spatial scan statistic. In this paper, we apply the Gini coefficient to normal-based spatial scan statistics. Through a simulation study, we evaluate the performance of the proposed method. We illustrate the method using a real data example of female colorectal cancer incidence rates in South Korea for the year 2009.

• The Journal of Korea Robotics Society
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• v.6 no.3
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• pp.237-246
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• 2011

Robots have been used in many fields due to its performance improvement and variety of its functionality, to the extent which robots can replace human tasks. Individual feature and better performance of robots are expected and required to be created. As their performances and functions have increased, systems have gotten more complicated. Multi mobile robots can perform complex tasks with simple robot system and algorithm. But multi mobile robots face much more complex driving problem than singular driving. To solve the problem, in this study, driving algorithm based on the energy method is applied to the individual robot in a group. This makes a cluster be in a formation automatically and suggests a cluster the automatic driving method so that they stably arrive at the target. The energy method mentioned above is applying attractive force and repulsive force to a special target, other robots or obstacles. This creates the potential energy, and the robot is controlled to drive in the direction of decreasing energy, which basically satisfies lyapunov function. Through this method, a cluster robot is able to create a formation and stably arrives at its target.

• Journal of the Semiconductor & Display Technology
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• v.23 no.3
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• pp.21-28
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• 2024

Unsupervised learning is a type of machine learning, and unlike supervised learning or reinforcement learning, a target value for input value is not given. Clustering is mainly used for such unsupervised learning. One of the representative methods of such clustering is K-means clustering. Since K-means clustering is a method of determining the number of clusters and continuing to find the central point of the data allocated to the cluster, there is a problem that the clustered group may not be the optimal cluster. In this study, particle swarm optimization algorithm, which determines the motion vector by adding various variables as well as the center point, is applied to K-means clustering. The improved K-means clustering makes it possible to move toward better outcome values even when the center of cluster no longer change. In the conventional clustering method, the center of the cluster moves to the center of the data belonging to the cluster, and clustering ends when the cluster does not change, so other characteristics other than the center value are excluded. Unlike the conventional clustering method, the improved clustering method uses a central value, an average value, and a random value as variables, and a particle swarm optimization algorithm that modifies the vector for each iteration is applied. As a result, improved clustering method derived a better result value than the existing clustering method in the group's fitness index, silhouette score.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.3
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• pp.166-171
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• 2023

Efficient energy management is a very important factor in sensor networks with limited resources, and cluster techniques have been studied a lot in this respect. However, a problem may occur in which energy use of the cluster header is concentrated, and when the cluster header is not evenly distributed over the entire area but concentrated in a specific area, the transmission distance of the cluster members may be large or very uneven. The transmission distance can be directly related to the problem of energy consumption. Since the energy of a specific node is quickly exhausted, the lifetime of the sensor network is shortened, and the efficiency of the entire sensor network is reduced. Thus, balanced energy consumption of sensor nodes is a very important research task. In this study, factors for balanced energy consumption by cluster headers and sensor nodes are analyzed, and a balancing distribution clustering method in which cluster headers are balanced distributed throughout the sensor network is proposed. The proposed cluster method uses multi-hop routing to reduce energy consumption of sensor nodes due to long-distance transmission. Existing multi-hop cluster studies sets up a multi-hop cluster path through a two-step process of cluster setup and routing path setup, whereas the proposed method establishes a hierarchical cluster routing path in the process of selecting cluster headers to minimize the overhead of control messages.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.1
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• pp.45-50
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• 2013

Sensors have limited resources in sensor networks, so efficient use of energy is important. Representative clustering methods, LEACH, LEACHC, TEEN generally use direct transmission methods from cluster headers to a sink node to pass collected data. If clusters are located at a long distance from the sink node, the cluster headers exhaust a lot of energy in order to transfer the data. As a consequence, the life of sensors is shorten and re-clustering is needed. In the process of clustering, sensor nodes consume some energy and the energy depletion of the cluster headers meet another energy exhaustion. A method of transferring data from cluster headers to the sink using neighbor clusters is needed for saving energy. In this paper, we propose a novel routing method using a multi-hop transmission method in cluster sensor networks. This method uses the topology matrix which presents cluster topology. One-hop routing and two-hop routing are proposed in order to increase the energy efficiency.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.11A
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• pp.1064-1072
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• 2005

For the efficient operation of sensor networks, it is very important to design sensor networks for sensors to utilize their energies in very effective ways. Cluster-based routing schemes such as LEACH can achieve their energy efficiencies by delivering data between cluster heads and sensor nodes. In those cluster-based schemes, cluster reconfiguration algorithm is one of the most critical issues to achieve longer operation lifetime of sensor networks. In this paper, we propose a new energy efficient cluster reconfiguration algorithm. Proposed method does not require any location or energy information of sensors, and can configure clusters with fair cluster regions such that all the sensors in a sensor network can utilize their energies equally. The performances of the proposed scheme have been compared with LEACH and LEACH-C.

• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.4
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• pp.39-60
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• 2003

In this study, we evaluate financial performance of 21 domestic life insurers using SAW (simple additive weighting), ELECTREII, cluster analysis respectively, and suggest a hybrid approach of combining cluster analysis and ELECTREII to reclassify the life insurers into more meaningful groups according to their respective financial features. We also perform the sensitivity analysis employing ANOVA and Tukey's test to examine the robustness of ELECTREII, which would be influenced by decision maker's subjective preference parameters. Consequently, it is shown that ELECTREII turns out to be a flexible method providing decision makers with useful ranking Information especially under fuzzy decision making situation with incomparable alternatives, and hence it can serve as a complementary method to overcome the weakness of classical cluster analysis.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.7
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• pp.51-60
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• 1997

Prototype based methods are commonly used in cluster analysis and the results may be highly dependent on the prototype used. In this paper, we propose a fuzzy clustering method that involves adaptively expanding convex polytopes. Thus, the dependency on the use of prototypes can be eliminated. The proposed method makes it possible to effectively represent an arbitrarily distributed data set without a priori knowledge of the number of clusters in the data set. Specifically, nonlinear membership functions are utilized to determine whether a new cluster is created or which vertex of the cluster should be expanded. For this, the membership function of a new vertex is assigned according to not only a distance measure between an incoming pattern vector and a current vertex, but also the amount how much the current vertex has been modified. Therefore, cluster expansion can be only allowed for one cluster per incoming pattern. Several experimental results are given to show the validity of our mehtod.