• Journal of the Korean Society of Clothing and Textiles
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• v.19 no.6
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• pp.924-932
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• 1995

Concept of the comfort and fitness has become a major concern in the basic function of the ready.made clothes. Until now ready-made clothes were not made by on the basis of the bodytype, but by the body size only This research was performed to classify and characterize the bodytypes of Korean adult males. Sample size was 1290 subjects and their age range was from 19 to 54 years old. 25 variables from the photographic data were applied to analyze the bodytype of trunk. Data were analyzed by the multivariate method, especially factor and cluster analysis. The groups forming a cluster can be subdivided into 5 sets by crosstabulation extracted by the hierarchical cluster analysis. 5 bodytypes classified by the photographic sources could be combined with the anthropometric data and were demonstrated with 5 silhouette. Type 3 and 4 in trunk were dominant and were composed of the majority of 55.6% of the subjects. Bodytypes of Korean males were influenced by the degree of posture erectness and of curvature of the front side of the body in waist and abdomen.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.369-374
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• 2004

A cluster merging algorithm that merges convex clusters resulted by the Fuzzy Convex Clustering(FCC) method into non-convex clusters was proposed. This was achieved by proposing a fast and reliable distance measure between two convex clusters using Support Vector Machines(SVM) to improve accuracy and speed over other existing conventional methods. In doing so, it was possible to reduce cluster number without losing its representation of the data. In this paper, results for several data sets are given to show the validity of our distance measure and algorithm.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.39.3-39.3
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• 2016

About one tenth of dwarf elliptical galaxies found in the Virgo cluster have a disk component, and some of them even possess substructures such as bars, lens, and spiral arms. We use N-body simulations to study the formation of these non-axisymmetric features in disky dwarf elliptical galaxies. By mimicking VCC 856, a bulgeless dwarf galaxy with embedded faint spiral arms, we construct 11 sets of initial conditions with slight dynamical variations based on observational data. Our standard model starts slowly to form a bar at ~3 Gyr and then undergoes buckling instability that temporarily weakens the bar although the bar strength continues to grow afterward. We find 9 of our models are unstable to bar formation and undergo buckling instability. This suggests that disky dwarf elliptical galaxies are intrinsically unstable to form bars, accounting for a population of barred dwarf galaxies in the outskirts of the Virgo cluster. To understand the origin of the faint grand-design spiral arms, we additionally construct 6 sets of models that undergo tidal interactions with their neighbors. We find that faint spiral arms consistent with observations develop when tidal forcing is relatively weak although strong encounter still results in bar formation. We discuss our results in light of the dynamical evolution of dwarf elliptical galaxies including mergers.

• The Korean Journal of Mycology
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• v.42 no.2
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• pp.159-164
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• 2014

For development of a method for differentiation of Pleurotus eryngii cultivars, simple sequence repeats (SSR) from whole genomic DNA sequence analysis was used for genotyping and two multiplex-SSR primer sets were developed. These SSR primer sets were employed to distinguish 12 cultivars and strains. Five polymorphic markers were selected based on the genotyping results. PCR using each primer produced one to four distinct bands ranging in size from 200 to 300 bp. Polymorphism information content (PIC) values of the five markers were in the range of 0.6627 to 0.6848 with an average of 0.6775. Unweighted pairgroup method with arithmetic mean clustering analysis based on genetic distances using five SSR markers classified 12 cultivars into two clusters. Cluster I and II were comprised of four and eight cultivars, respectively. Two multiplex sets, Multi-1 (SSR312 and SSR366) and Multi-2 (SSR178 and SSR277) completely discriminated 12 cultivars and strains with 21 alleles and a PIC value of 0.9090. These results might be useful in providing an efficient method for the identification of P. eryngii cultivars with separate PCR reactions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.3
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• pp.165-170
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• 2011

Due to advances in hardware performance, user-friendly interfaces are becoming one of the major concerns in information systems. Linguistic conversation is a very natural way of human communications. Fuzzy techniques have been employed to liaison the discrepancy between the qualitative linguistic terms and quantitative computerized data. This paper deals with linguistic queries using clustering results on data sets, which are intended to retrieve the close clusters or distant clusters from the clustering results. In order to support such queries, a fuzzy technique-based method is proposed. The method introduces distance membership functions, namely, close and distant membership functions which transform the metric distance between two objects into the degree of closeness or farness, respectively. In order to measure the degree of closeness or farness between two clusters, both cluster closeness measure and cluster farness measure which incorporate distance membership function and cluster memberships are considered. For the flexibility of clustering, fuzzy clusters are assumed to be formed. This allows us to linguistically query close or distant clusters by constructing fuzzy relation based on the measures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3714-3732
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• 2019

With the rapid development of network, Intrusion Detection System(IDS) plays a more and more important role in network applications. Many data mining algorithms are used to build IDS. However, due to the advent of big data era, massive data are generated. When dealing with large-scale data sets, most data mining algorithms suffer from a high computational burden which makes IDS much less efficient. To build an efficient IDS over big data, we propose a classification algorithm based on data clustering and data reduction. In the training stage, the training data are divided into clusters with similar size by Mini Batch K-Means algorithm, meanwhile, the center of each cluster is used as its index. Then, we select representative instances for each cluster to perform the task of data reduction and use the clusters that consist of representative instances to build a K-Nearest Neighbor(KNN) detection model. In the detection stage, we sort clusters according to the distances between the test sample and cluster indexes, and obtain k nearest clusters where we find k nearest neighbors. Experimental results show that searching neighbors by cluster indexes reduces the computational complexity significantly, and classification with reduced data of representative instances not only improves the efficiency, but also maintains high accuracy.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.481-486
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• 2021

Machine learning algorithms adopt criterion function as a key component to measure the quality of their model derived from data. Cluster analysis also uses this function to rate the clustering result. All the criterion functions have in general certain types of favoritism in producing high quality clusters. These clusters are then described by attributes and their values. Category utility and partition utility play an important role in cluster analysis. These are fully analyzed in this research particularly in terms of how they are related to the favoritism in the final results. In this research, several data sets are selected and analyzed to show how different results are induced from these criterion functions.

• 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 Korea Multimedia Society
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• v.11 no.5
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• pp.633-640
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• 2008

Sensor node clustering is one of the most popular research topics to reduce the energy of sensor nodes in wireless sensor networks. Previous researches, however, did not consider prediction effects of sensed environment information on TDMA scheduling of a cluster, resulting energy inefficiency. In this paper, we suggest an energy efficient cluster-based scheduling scheme that can be applied flexibly to many environment information systems. This scheme reflects the environment information obtained at the application layer to the MAC layer to set up the schedule of a cluster. The application layer information sets up the scheduling referring to the similarity of sensed data of cluster head. It determines the data transmission considering the result of similarity. We show that our scheme is more efficient than LEACH and LEACH-C in energy, which are popular clustering schemes, through simulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.354-359
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• 2008

Clustering is a method to classify the given data set with same property into several classes. To cluster data, many methods such as K-Means, Fuzzy C-Means(FCM), Mountain Method(MM), and etc, have been proposed and used. But the clustering results of conventional methods are sensitively influenced by initial values given for clustering in each method. Especially, FCM is very sensitive to noisy data, and cluster center distortion phenomenon is occurred because the method dose clustering through minimization of within-clusters variance. In this paper, we propose a clustering method which reduces cluster center distortion through merging the nearest data based on the data weight, and not being influenced by initial values. We show the effectiveness of the proposed through experimental results applied it to various types of data sets, and comparison of cluster centers with those of FCM.