• The KIPS Transactions:PartD
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• v.17D no.3
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• pp.175-184
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• 2010

Experiments and research on genes have become very convenient by using DNA chips, which provide large amounts of data from various experiments. The data provided by the DNA chips could be represented as a two dimensional matrix, in which one axis represents genes and the other represents samples. By performing an efficient and good quality clustering on such data, the classification work which follows could be more efficient and accurate. In this paper, we use a bio-inspired algorithm called the Particle Swarm Optimization algorithm to propose an efficient clustering mechanism for large amounts of DNA chip data, and show through experimental results that the clustering technique using the PSO algorithm provides a faster yet good quality result compared with other existing clustering solutions.

• Genomics & Informatics
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• v.9 no.2
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• pp.89-91
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• 2011

DNA chips are becoming increasingly popular as a convenient way to perform vast amounts of experiments related to genes on a single chip. And the importance of analyzing the data that is provided by such DNA chips is becoming significant. A very important analysis on DNA chip data would be clustering genes to identify gene groups which have similar properties such as cancer. Clustering data for DNA chips usually deal with a large search space and has a very fuzzy characteristic. The Particle Swarm Optimization algorithm which was recently proposed is a very good candidate to solve such problems. In this paper, we propose a clustering mechanism that is based on the Particle Swarm Optimization algorithm. Our experiments show that the PSO-based clustering algorithm developed is efficient in terms of execution time for clustering DNA chip data, and thus be used to extract valuable information such as cancer related genes from DNA chip data with high cluster accuracy and in a timely manner.

• Publications of The Korean Astronomical Society
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• v.27 no.4
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• pp.343-344
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• 2012

We present the first measurement of the angular two-point correlation function for AKARI $90{\mu}m$ point sources, detected outside of the Milky Way plane and selected as candidates for extragalactic sources. This is the first measurement of the large-scale angular clustering of galaxies selected in the far-infrared after IRAS. We find a positive clustering signal in both hemispheres extending up to ~ 40 degrees, without any significant fluctuations at larger scales. The observed correlation function is well fitted by a power law function. However, southern galaxies seem to be more strongly clustered than northern ones and the difference is statistically significant. The reason for this difference - technical or physical - is still to be found.

• Knowledge Management Research
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• v.10 no.4
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• pp.17-30
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• 2009

This study is concerned with suggesting a new clustering algorithm to evaluate the value of papers which were supported by research grants by Korea Research Fund (KRF). The algorithm is based on an extended version of a conventional PSO (Particle Swarm Optimization) mechanism. In other words, the proposed algorithm is based on integration of k-means algorithm and simulated annealing mechanism, named KASA-PSO. To evaluate the robustness of KASA-PSO, its clustering results are evaluated by research grants experts working at KRF. Empirical results revealed that the proposed KASA-PSO clustering method shows improved results than conventional clustering method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3512-3528
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• 2016

A network with high mobility nodes or vehicles is vehicular ad hoc Network (VANET). For improvement in communication efficiency of VANET, many techniques have been proposed; one of these techniques is vehicular node clustering. Cluster nodes (CNs) and Cluster Heads (CHs) are elected or selected in the process of clustering. The longer the lifetime of clusters and the lesser the number of CHs attributes to efficient networking in VANETs. In this paper, a novel Clustering algorithm is proposed based on Ant Colony Optimization (ACO) for VANET named ACONET. This algorithm forms optimized clusters to offer robust communication for VANETs. For optimized clustering, parameters of transmission range, direction, speed of the nodes and load balance factor (LBF) are considered. The ACONET is compared empirically with state of the art methods, including Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO) based clustering techniques. An extensive set of experiments is performed by varying the grid size of the network, the transmission range of nodes, and total number of nodes in network to evaluate the effectiveness of the algorithms in comparison. The results indicate that the ACONET has significantly outperformed the competitors.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.986-1016
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• 2019

Mobile user interface pattern (MUIP) is a kind of structured representation of interaction design knowledge. Several studies have suggested that MUIPs are a proven solution for recurring mobile interface design problems. To facilitate MUIP selection, an effective clustering method is required to discover hidden knowledge of pattern data set. In this paper, we employ the semi-supervised kernel fuzzy c-means clustering (SSKFCM) method to cluster MUIP data. In order to improve the performance of clustering, clustering parameters are optimized by utilizing the global optimization capability of particle swarm optimization (PSO) algorithm. Since the PSO algorithm is easily trapped in local optima, a novel PSO algorithm is presented in this paper. It combines an improved intuitionistic fuzzy entropy measure and a new population search strategy to enhance the population search capability and accelerate the convergence speed. Experimental results show the effectiveness and superiority of the proposed clustering method.

• Proceedings of the KIEE Conference
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• 2008.10c
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• pp.86-89
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• 2008

There are some modified methods such as K-means Clustering Particle Swarm Optimization and Niching Particle Swarm Optimization based on PSO which aim to locate all optima in multimodal functions. K-means Clustering Particle Optimization could locate all optima of functions with finite number of optima. Niching Particle Swarm Optimization is able to locate all of optima but high computing time. Because of those disadvantages, we proposed a new method that could locate all of optima with reasonal time. We applied our method and others as well to analytic functions. By comparing the outcomes, it is shown that our method is significantly more effective than the two others.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.1
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• pp.184-192
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• 2011

In this study, we introduce a identification methodology for FCM-based fuzzy model. The two underlying design mechanisms of such networks involve Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on FCM clustering method for efficient processing of data and the optimization of model was carried out using PSO. The premise part of fuzzy rules does not construct as any fixed membership functions such as triangular, gaussian, ellipsoidal because we build up the premise part of fuzzy rules using FCM. As a result, the proposed model can lead to the compact architecture of network. In this study, as the consequence part of fuzzy rules, we are able to use four types of polynomials such as simplified, linear, quadratic, modified quadratic. In addition, a Weighted Least Square Estimation to estimate the coefficients of polynomials, which are the consequent parts of fuzzy model, can decouple each fuzzy rule from the other fuzzy rules. Therefore, a local learning capability and an interpretability of the proposed fuzzy model are improved. Also, the parameters of the proposed fuzzy model such as a fuzzification coefficient of FCM clustering, the number of clusters of FCM clustering, and the polynomial type of the consequent part of fuzzy rules are adjusted using PSO. The proposed model is illustrated with the use of Automobile Miles per Gallon(MPG) and Boston housing called Machine Learning dataset. A comparative analysis reveals that the proposed FCM-based fuzzy model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.163-166
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• 2003

In this study, a grouping of particle-size distributions(PSDs) by means of the fuzzy c-means clustering method(FCM) was presented. The classification was performed with the whole and the major soil series representing pedological origin. In case of the major soil series, PSDs were clustered as $2{\sim}4$ groups and the characteristics of clustering results were quite different between the soil series. It was found that the characteristics of PSDs at center of each class can be explained by formation process of each soil series. In case of whole soil data, PSDs were classified to 8 classes in which 4 classes were single mode and 4 classes were bimode distributions. Through this study, it is concluded that pedogenetic process is a plausible explanation for grain size distribution of soils.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.60-63
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• 2008

바이오칩을 이용하여 유전자를 분석하는데 이때 바이오 칩 분석 시스템을 이용한다. 바이오 칩은 유전자와 실험의 두 축으로 이루어져 있으며 바이오 칩 분석 시스템을 사용하여 바이오 칩에서 자료를 추출하고 필요한 정보를 얻기 위해 데이터를 분석하는 시스템이다. 데이터를 분석하는 기법 중 클러스터링을 사용하는데 유사한 유전자들을 찾아 내어 정해놓은 클러스터로 정의한다. 같은 클러스터 안에 있는 유전자들은 서로 비슷한 성질을 가지고 있기 때문에 사용자들은 이 바이오 칩 으로부터 나온 정보를 효율적이게 사용할 수 있다. 더욱 효율적으로 사용하기 위해 본 논문에서는 방대한 양의 데이터의 최적화에 효율적인 생태계 모방 알고리즘 Particle Swarm Optimization을 이용하여 데이터들을 클러스터링을 하여 분류하는 시스템을 기술하고 있다.