• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.4
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• pp.79-86
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• 2009

In this paper, we propose a new clustering scheme that provides optimal data aggregation effect and reduces energy consumption of nodes by considering the density of nodes when forming clusters. Since the size of the cluster is determined to ensure optimal data aggregation rate, our scheme reduces transmission range and minimizes interference between clusters. Moreover, by clustering using locally adjacent nodes and aggregating data received from cluster members, we reduce energy consumption of nodes. Through simulation, we confirmed that energy consumption of the whole network is minimized and the sensor network life-time is extended. Moreover, we show that the proposed clustering scheme improves the performance of network compared to previous LEACH clustering scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3121-3143
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• 2019

Segmentation plays an important role in the field of image processing and computer vision. Intuitionistic fuzzy C-means (IFCM) clustering algorithm emerged as an effective technique for image segmentation in recent years. However, standard fuzzy C-means (FCM) and IFCM algorithms are sensitive to noise and initial cluster centers, and they ignore the spatial relationship of pixels. In view of these shortcomings, an improved algorithm based on IFCM is proposed in this paper. Firstly, we propose a modified non-membership function to generate intuitionistic fuzzy set and a method of determining initial clustering centers based on grayscale features, they highlight the effect of uncertainty in intuitionistic fuzzy set and improve the robustness to noise. Secondly, an improved nonlinear kernel function is proposed to map data into kernel space to measure the distance between data and the cluster centers more accurately. Thirdly, the local spatial-gray information measure is introduced, which considers membership degree, gray features and spatial position information at the same time. Finally, we propose a new measure of intuitionistic fuzzy entropy, it takes into account fuzziness and intuition of intuitionistic fuzzy set. The experimental results show that compared with other IFCM based algorithms, the proposed algorithm has better segmentation and clustering performance.

• Journal of Preventive Medicine and Public Health
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• v.41 no.6
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• pp.427-433
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• 2008

Objectives: This study was performed to assess the association between physical activity and the clustering of metabolic abnormalities among Korean children. The effect of substituting moderate to vigorous physical activity for the time spent in inactivity was examined as well. Methods: The study subjects were comprised of 692 (354 boys, 338 girls) 4th grade elementary school students. We used a modified form of the physical activity questionnaire that was developed in the Five-City Project. The subjects with clustering of metabolic abnormalities were defined as having two or more of the following five characteristics: waist circumference ${\geq}90\;%$, systolic or diastolic blood pressure ${\geq}90\;%$, fasting glucose ${\geq}110\;mg/dl$, triglycerides ${\geq}110\;mg/dl$ and HDL cholesterol ${\geq}40\;mg/dl$. We calculated the odds ratios to assess the effect of substituting moderate to vigorous physical activity for time spent in inactivity. Results: The risk of clustered metabolic abnormalities was inversely correlated with the increased time spent on moderate to vigorous physical activity, but the correlation was not significant. The odds ratio for clustering of metabolic abnormalities that represented the effect of substituting moderate to vigorous physical activity for 30minutes of sedentary activity was 0.87(95% Cl=0.76-1.01). Conclusions: These findings suggest that substituting moderate to vigorous physical activity for sedentary activity could decrease the risk of clustered metabolic abnormalities.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.3
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• pp.356-360
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• 2010

Recently, there have been many research efforts on privacy-preserving data mining. In privacy-preserving data mining, accuracy preservation of mining results is as important as privacy preservation. Random perturbation privacy-preserving data mining technique is known to well preserve privacy. However, it has a problem that it destroys distance orders among time-series. In this paper, we propose a notion of the noise averaging effect of piecewise aggregate approximation(PAA), which can be preserved the clustering accuracy as high as possible in time-series data clustering. Based on the noise averaging effect, we define the PAA distance in computing distance. And, we show that our PAA distance can alleviate the problem of destroying distance orders in random perturbing time series.

• Bulletin of the Korean Chemical Society
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• v.19 no.3
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• pp.354-358
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• 1998

The effect of thermal conditions on the clustering of sulfonated polystyrene ionomers was investigated. It was found that when the zinc-sulfonated ionomer was dried above a matrix glass transition temperature (Tg), the cluster Tg was observed at ca. 310 ℃, which is ca. 45 ℃ higher than that for the ionomer dried below the matrix Tg. This difference is believed to be the result of the increase in chain mobility at higher temperatures, which improves the multiplet formation and clustering; thus the cluster Tg increases. In the lithium ionomer case, however, the increase in the cluster Tg was ca. 6 ℃ upon annealing. From the results, it was suggested that in the zinc ionomer, the zinc ion is soft and divalent, which results in weaker interactions in multiplets, and thus decreases the stability of the multiplets. Therefore, the thermal effect is more significant for the zinc ionomers than for the lithium ionomers.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.431-434
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• 2003

In this paper we introduce a modified objective function for fuzzy c-means clustering with logistic regression model in the presence of noise cluster. The logistic regression model is commonly used to describe the effect of one or several explanatory variables on a binary response variable. In real application there is very often no sharp boundary between clusters so that fuzzy clustering is often better suited for the data.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.699-706
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• 2016

Outlier detection methods without performing a test often do not succeed in detecting multiple outliers because they are structurally vulnerable to a masking effect or a swamping effect. This paper considers testing procedures supplemented to a clustering-based method of identifying the group with a minority of the observations as outliers. One of general steps is performing a variety of t-test on individual outlier-candidates. This paper proposes a sequential procedure for searching for outliers by changing cutoff values on a cluster tree and performing a test on a set of outlier-candidates. The proposed method is illustrated and compared to existing methods by an example and Monte Carlo studies.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.735-742
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• 2003

In the information retrieval system, document clustering technique is to provide user convenience and visual effects by rearranging documents according to the specific topics from the retrieved ones. In this paper, we clustered documents using K-Means algorithm and present the effect of index terms weighting scheme on the document clustering. To verify the experiment, we applied Latent Semantic Indexing approach to illustrate the clustering results and analyzed the clustering results in 2-dimensional space. Experimental results showed that in case of applying local weighting, global weighting and normalization factor, the density of clustering is higher than those of similar or same weighting schemes in 2-dimensional space. Especially, the logarithm of local and global weighting is noticeable.

• Journal of computational fluids engineering
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• v.21 no.4
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• pp.46-53
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• 2016

Direct numerical simulation is conducted to observe the behavior of microbubbles in isotropic turbulence. Navier-Stokes equation and the motion of equation for microbubbles are solved with periodic boundary condition in a cube domain. Vorticity contour, enstrophy ratio, relative reduction of bubble rise velocity, and the closest distance of particles are investigated for various Stokes numbers and gravity factors to understand clustering of microbubbles. Also, clustering due to the effect of the lift force is investigated.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.201-206
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• 1998

In this paper, we have considered the method for clustering land cover types over the East Asia from AVHRR data. The feature vectors such that maximum NDVI, amplitude of NDVI, mean NDVI, and NDVI threshold are extracted from the 10-day composite by maximum value composite(MVC) for reducing the effect of cloud contaninations. To find the land cover clusters given by the feature vectors, we are adapted the self-organizing feature map(SOFM) clustering which is the mapping of an input vector space of n-dimensions into a one - or two-dimensional grid of output layer. The approach is to find first the clusters by the first layer SOFM and then merge several clusters of the first layer to a large cluster by the second layer SOFM. In experiments, we were used the 8-km AVHRR data for two years(1992-1993) over the East Asia.