• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.517-524
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• 2004

The fuzzy set theory has been wide used in clustering of machine learning with data mining since fuzzy theory has been introduced in 1960s. In particular, fuzzy C-means algorithm is a popular fuzzy clustering algorithm up to date. An element is assigned to any cluster with each membership value using fuzzy C-means algorithm. This algorithm is affected from the location of initial cluster center and the proper cluster size like a general clustering algorithm as K-means algorithm. This setting up for initial clustering is subjective. So, we get improper results according to circumstances. In this paper, we propose a cluster merging using enhanced density based fuzzy C-means clustering algorithm for solving this problem. Our algorithm determines initial cluster size and center using the properties of training data. Proposed algorithm uses grid for deciding initial cluster center and size. For experiments, objective machine learning data are used for performance comparison between our algorithm and others.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.77.2-77.2
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• 2015

Galaxies are known to evolve passively in the cluster environment. Indeed, much evidence for HI stripping has been found in cluster galaxies to date, which is likely to be connected to their low star formation rate. What is still puzzling however, is that the molecular gas, which is believed to be more directly related to star formation, shows no significant difference in its fraction between the cluster population and the field galaxies. Therefore, HI stripping alone does not seem to be enough to fully understand how galaxies become passive in galaxy clusters. Intriguingly, our recent high resolution CO study of a subsample of Virgo spirals which are undergoing strong ICM pressure has revealed a highly disturbed molecular gas morphology and kinematics. The morphological and kinematical peculiarities in their CO data have many properties in common with those of HI gas in the sample, indicating that strong ICM pressure in fact can have impacts on dense gas deep inside of a galaxy. This implies that it is the molecular gas conditions rather than the molecular gas stripping which is more responsible for quenching of star formation in cluster galaxies. In this study, using multi transitions of 12CO and 13CO, we investigate the density and temperature distributions of CO gas of a Virgo spiral galaxy, NGC 4402 to probe the physical and chemical properties of molecular gas and their relations to star formation activities.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.285-292
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• 2000

In general, exploratory data analysis consists of three processes: i) assessment of clustering tendency, ii) cluster analysis, and iii) cluster validation. This analysis method requiring a number of iterations of step ii) and iii) to converge is computationally inefficient. In this paper, we propose a density function-based approximate fuzzy clustering method with a hierachical structure which consosts of two phases: Phase I is a features(i.e., number of clusters and cluster centers) extraction process based on the tendency assessment of a given data and Phase II is a standard FCM with the cluster centers intialized by the results of the Phase I. Numerical examples are presented to show the validity of the proposed clustering method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.248-249
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• 2015

Clustering is one of the most important unsupervised learning methods that clusters data into homogeneous groups. However, cluster centers tend leaning to high density clusters because clustering is based on the distances between data points and cluster centers. In this paper, a modified clustering method forcing cluster centers to be apart by introducing a center-scattering term in the Fuzzy C-Means objective function is introduced. The proposed method converges more to real centers with small number of iterations compared to the original one. All the strengths can be verified with experimental results.

• Journal of Information Technology Services
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• v.9 no.3
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• pp.95-106
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• 2010

Many researches have been performed to increase energy-efficiency in wireless sensor networks. One of primary research topics is about clustering protocols, which are adopted to configure sensor networks in the form of hierarchical structures by grouping sensor nodes into a cluster. However, legacy clustering protocols do not propose detailed methods from the perspective of implementation to determine a cluster's boundary and configure a cluster, and to communicate among clusters. Moreover, many of them involve assumptions inappropriate to apply those to a sensor field. In this paper, we have designed and implemented a new T-Clustering (Top-down Clustering) protocol, which takes into considerations a node's density, a distance between cluster heads, and remained energy of a node all together. Our proposal is a sink-node oriented top-down clustering protocol, and can form uniform clusters throughout the network. Further, it provides re-clustering functions according to the state of a network. In order to verify our protocol's feasibility, we have implemented and experimented T-Clustering protocol on Crossbow's MICAz nodes which are executed on TinyOS 2.0.2.

• Journal of the Korean earth science society
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• v.38 no.5
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• pp.333-344
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• 2017

We present the environmental dependence of the luminosity-size relation of galaxies in the local universe (z < 0.01) along with their dependence on galaxy morphology represented by five broad types (E, dEs, S0, Sp, and Irr). The environmental parameters we consider are the local background density and the group/cluster membership together with the clustercenteric distance for the Virgo cluster galaxies. We derive the regression coefficient (${\beta}$), i.e., the slope of the line representing the least-squares fitting to the data and the Pearson correlation coefficient (c.c.) representing the goodness of the least-squares fit along with the confidence interval from bootstrap resampling. We find no significant dependence of the luminosity-size relation on galaxy morphology. However, there is a weak dependence of the luminosity-size relations on the environment of galaxies, in the sense that galaxies in the low density environment have shallower slopes than galaxies in the high density regions except for elliptical galaxies that show an opposite trend.

• Journal of Industrial Convergence
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• v.22 no.1
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• pp.1-20
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• 2024

Regarding the discourse on the correlation between governmental financial support and firm performance, much emphasis has been placed on the role of individual corporate characteristics as well as spatial features. However, there is a notable scarcity of empirical research examining the integrated impact of corporate and cluster characteristics on managerial performance. This study addresses this gap by empirically analyzing the financial and non-financial outcomes resulting from specific allocations of governmental financial support. Additionally, it explores corporate and cluster characteristics predicted to moderate the influence between governmental financial support and firm performance. The analysis employs a two-level hierarchical linear model (HLM) at individual and group levels. The data, reorganized based on business registration numbers at the firm and cluster levels, ultimately utilized panel data from 83,395 firms and 641 clusters. The research findings indicate that governmental financial support demonstrates a positive effect (+) on both sales and patents for firms, suggesting its effectiveness in complementing market failures. Results from the hierarchical linear model analysis show that when combined with human capital capacity, absorptive capacity, and cluster network density, governmental financial support exhibits significant positive effects on sales. This study contributes theoretical and practical insights by analyzing the relationship between governmental financial support and firm performance using a two-level hierarchical linear model. It highlights the role of corporate characteristics such as human capital and absorptive capacity, along with cluster characteristics like cluster network density, in moderating the effects of governmental financial support on firm performance.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.1
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• pp.57.1-57.1
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• 2011

It has been suggested that merging plays an important role in the formation and the evolution of early-type galaxies (ETGs). Optical-NIR color gradients of ETGs in high density environments are found to be less steep than those of ETGs in low density environments, hinting frequent merger activities in ETGs in high density environments. In order to examine if the flat color gradients are the result of dry mergers, we studied the relations between merging features, color gradient, and environments of 281 low redshift ETGs selected from Sloan Digital Sky Survey (SDSS) Stripe82. The sample contains 222 relaxed ETGs, 38 ETGs with tidal features, 10 galaxies with dust features and 11 galaxies with tidal and dust features, and Near Infrared (NIR) images are taken from UKIRT Infrared Deep Sky Survey (UKIDSS) Large Area Survey (LAS). We find that r-K color gradients of field sample galaxies are steeper than those of sample ETGs within cluster environments. For the field sample galaxies, a relatively large number of galaxies with peculiar features contribute to the steeper color gradients, while the absence of these peculiar early-type galaxies make color gradients of the cluster sample galaxies intact. In high density environment, ETGs are already evolved and relaxed, resulting flat color gradients. However, in low density environments, a majority of ETGs undergone merging recently which makes the color gradients steep.

• Journal of the Korean Chemical Society
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• v.67 no.1
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• pp.13-18
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• 2023

We evaluate electron density functional theories for the computation of 0-1 and 1-2 transition energies of local OH stretching motion of water molecules in condensed phases. By examining thirteen density functionals and nine sets of basis functions, it was found that the optimal combination that predicts the transition energies highly correlated with those calculated by the coupled cluster theory, CCSD(T), is the hybrid density functional theory developed by Head-Gordon group, ωB97X(D)/6-31+G^*.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.1-7
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• 2020

Fuzzy clustering, represented by FCM(Fuzzy C-Means), is a simple and efficient clustering method. However, the object function in FCM makes clusters affect clustering results proportional to the density of clusters, which can distort clustering results due to density difference between clusters. One method to alleviate this density problem is EDI-FCM(Extended Density-Independent FCM), which adds additional terms to the objective function of FCM to compensate for the density difference. In this paper, proposed is an enhanced EDI-FCM using regularization, Regularized EDI-FCM. Regularization is commonly used to make a solution space smooth and an algorithm noise insensitive. In clustering, regularization can reduce the effect of a high-density cluster on clustering results. The proposed method converges quickly and accurately to real centers when compared with FCM and EDI-FCM, which can be verified with experimental results.