• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.6
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• pp.2815-2822
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• 2011

Clustering method in wireless sensor networks is the technique that forms the cluster to aggregate the data and transmit them at the same time that they can use the energy efficiently. In this paper, I propose the hierarchical unequal clustering method using cluster group model. This divides the entire network into two layers. The data aggregated from layer 2 consisted of cluster group is sent to layer 1, after re-aggregation the total data is sent to base station. This method decreases whole energy consumption by using cluster group model with multi-hop communication architecture. Hot spot problem can be solved by establishing unequal cluster. I also show that proposed hierarchical unequal clustering method is better than previous clustering method at the point of network energy efficiency.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1525-1535
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• 2008

High Altitude Platform (HAP) based networks deploy network infrastructures of Mobile Base Station (MBS) in a form of Unmanned Aerial Vehicle (UAV) at stratosphere in order to build network configuration. The ultimate goal of HAP based network is a wireless network service for wide area by deploying multiple MBS for such area. In this paper we assume multiple UAVs over designated area and solve the MBS placement and coverage problem by clustering the mobile ground nodes. For this study we assumed area around Cheju island and nearby naval area where multiple mobile and fixed nodes are deployed and requires HAP based networking service. By simulation, visual results of stratospheric MBS placement have been presented. These results include clustering, MBS placement and coverage as well as dynamic reclustering according to the movement of mobile ground nodes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.4
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• pp.721-727
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• 2016

In this paper, in order to resolve the problem of reduction for D2D (device to device) advertisement dissemination efficiency of conventional dissemination algorithms, we here propose several clustering algorithms (modified single linkage algorithm (MSL), K-means algorithm, and expectation maximization algorithm with Gaussian mixture model (EM)) based advertisement dissemination algorithms to improve advertisement dissemination efficiency in D2D communication networks. Target areas are clustered in several target groups by the proposed clustering algorithms. Then, D2D advertisements are consecutively distributed by using a routing algorithm based on the geographical distribution of the target areas and a relay selection algorithm based on the distance between D2D sender and D2D receiver. Via intensive MATLAB simulations, we analyze the performance excellency of the proposed algorithms with respect to maximum number of relay transmissions and D2D user density ratio in a target area and a non-target area.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.688-694
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• 2005

In this Paper, we Propose a new neuro-fuzzy modeling using clustering-based learning method. In the proposed clustering method, number of clusters is automatically inferred and its parameters are optimized simultaneously, Also, a neuro-fuzzy model is learned based on clustering information at same time. In the previous modelling method, clustering and model learning are performed independently and have no exchange of its informations. However, in the proposed method, overall neuro-fuzzy model is generated by using both clustering and model learning, and the information of modelling output is used to clustering of input. The proposed method improve the computational load of modeling using Subtractive clustering method. Simulation results show that the proposed method has an effectiveness compared with the previous methods.

• Journal of KIISE:Information Networking
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• v.32 no.1
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• pp.68-79
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• 2005

The popularity of Web service is ever increasing. As the number of services and clients continue to increase, the problem of providing a system that scales with this increase is becoming more difficult. A costly and ineffective method is to buy a new system that is more powerful every time the load becomes unbearable. h more cost effective solution is to expand the system as the need arises. This is the approach taken in Web cluster systems. However, providing effective scalability in a Web cluster system is stil1 an open issue. In this study, we implement a Web cluster system based on Layer 7 switching technique on Linux. The implementation is based on a design proposed and implemented by Aron et al., but on the FreeBSD. Though the design li the same, due to the vast difference between the FreeBSD and Linux, the implementation presented in this paper is totally new. We also propose the Dual Scheduling (DS) load distribution algorithm that distributes the requests to the system resources by observing the variations in the request rate. We show through measurement on our implementation that the DS alorithm performs considerably bettor than previous algorithms.

• Journal of KIISE:Software and Applications
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• v.34 no.11
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• pp.971-983
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• 2007

Cellular Genetic Algorithms(CGAs) are a subclass of Genetic Algorithms(GAs) in which each individuals are placed in a given geographical distribution. In general, CGAs# population space is a regular network that has relatively long characteristic path length and high clustering coefficient in the view of the Networks Theory. Long average path length makes the genetic interaction of remote nodes slow. If we have the population#s path length shorter with keeping the high clustering coefficient value, CGAs# population space will converge faster without loss of diversity. In this paper, we propose Smallest-Small-World Cellular Genetic Algorithms(SSWCGAs). In SSWCGAs, each individual lives in a population space that is highly clustered but having shorter characteristic path length, so that the SSWCGAs promote exploration of the search space with no loss of exploitation tendency that comes from being clustered. Some experiments along with four real variable functions and two GA-hard problems show that the SSWCGAs are more effective than SGAs and CGAs.

• Journal of KIISE:Software and Applications
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• v.30 no.11
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• pp.1072-1079
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• 2003

This study is purported for suggesting a new clustering algorithm that enables incremental categorization of numerous documents. The suggested algorithm adopts the natures of the spherical k-means algorithm, which clusters a mass amount of high-dimensional documents, and the fuzzy ART(adaptive resonance theory) neural network, which performs clustering incrementally. In short, the suggested algorithm is a combination of the spherical k-means vector space model and concept vector and fuzzy ART vigilance parameter. The new algorithm not only supports incremental clustering and automatically sets the appropriate number of clusters, but also solves the current problems of overfitting caused by outlier and noise. Additionally, concerning the objective function value, which measures the cluster's coherence that is used to evaluate the quality of produced clusters, tests on the CLASSIC3 data set showed that the newly suggested algorithm works better than the spherical k-means by 8.04% in average.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.558-567
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• 2001

Recently, data mining applications require a large amount of high-dimensional data Most algorithms for data mining applications however, do not work efficiently of high-dimensional large data because of the so-called curse of dimensionality[1] and the limitation of available memory. To overcome these problems, this paper proposes a new cell-based clustering which is more efficient than the existing algorithms for high-dimensional large data, Our clustering method provides a cell construction algorithm for dealing with high-dimensional large data and a index structure based of filtering .We do performance comparison of our cell-based clustering method with the CLIQUE method in terms of clustering time, precision, and retrieval time. Finally, the results from our experiment show that our cell-based clustering method outperform the CLIQUE method.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.3
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• pp.81-88
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• 1996

Cluster analysis is based on partitioning a collection of data points into a number of clusters, where the data points in side a cluster have a certain degree of similarity and it is a fundamental process of data analysis. So, it has been playing an important role in solving many problems in pattern recognition and image processing. For these many clustering algorithms depending on distance criteria have been developed and fuzzy set theory has been introduced to reflect the description of real data, where boundaries might be fuzzy. If fuzzy cluster analysis is tomake a significant contribution to engineering applications, much more attention must be paid to fundamental questions of cluster validity problem which is how well it has identified the structure that is present in the data. Several validity functionals such as partition coefficient, claasification entropy and proportion exponent, have been used for measuring validity mathematically. But the issue of cluster validity involves complex aspects, it is difficult to measure it with one measuring function as the conventional study. In this paper, we propose four performance indices and the way to measure the quality of clustering formed by given learning strategy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.942-948
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• 2020

In this paper, we propose a clustered algorithm that possible more efficient user distinction within clustering using context-aware attribute information. In typically, the data provided to classify interrelationships within cluster information in the process of clustering data will be as a degrade factor if new or newly processing information is treated as contaminated information in comparative information. In this paper, we have developed a clustering algorithm that can extract user's recognition information to solve this problem in using K-means algorithm. The proposed algorithm analyzes the user's clustering attributed parameters from user clusters using accumulated information and clustering according to their attributes. The results of the simulation with the proposed algorithm showed that the user management system was more adaptable in terms of classifying and maintaining multiple users in clusters.