• Journal of Environmental Science International
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• v.12 no.12
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• pp.1215-1225
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• 2003

The purpose of this research was to investigate the characteristics of urban ecotopes and to classify ecotopes systematically from them. Total of 15 characteristics for classification of ecotopes were selected, and there were categorized 3 factors, that is abiotic, biotic and anthropological factors. The ecotope types in the study area were classified into 67. The classification of ecotope was made with SPSS for Windows Version 10.0 on the basis of the 15 characteristics. As the results of cluster analysis using the average linkage method between groups, groups of ecotope type were divided into 15 clusters. It was known that there was not a great difference in an affinity as the result of overlapping the maps of ecotope type and land use type. This research suggested characteristics for classification of ecotopes, but there was a limit to Set the objective method for grade classification because of lacking in the basic data, the research of characteristics will be accomplished continuously.

• The Korean Journal of Ecology
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• v.7 no.3
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• pp.119-131
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• 1983

The cluster analysis on the distribution of epiphytic lichens on the north, south, east and west slope of Mt. Hanra was carried out by three methods, sum of square algorithm (SSA), prinicipal component analysis (PCA) and multidimensional scaling method(MDS). Analysis of concentration (AOC) was used for the comparison between the lichen communities of north and south slope. The lichen species was identified 35 species by Hale and Culberson technique. The classification of sites by SSA method was divided into two areas in four slopes, and that of species by SSA, PCA and MDS methods was classified into three clusters in east slope, four clusters in south and west slope, and there clusters in north slope. The comparison between north and south slope of the distribution of lichens indicates that loight elevation of north slope (NH; 1600m~1900m) was similar to that of relative low elevation of south slope (SL; 1000m~1300m). The genus lichen, Anaptychia, Parmelia, Lobaria and peltigera was found as the dominant genus in both slopes.

• Journal of the Korean Society of Clothing and Textiles
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• v.17 no.4
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• pp.529-538
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• 1993

This study was intended to identify the hierarchy of clothing images, which is expected to be helpful in style classification and product positioning. A questionnaire consisted of 110 words expressing clothing images was developed, and eight clothing photographs were selected as stimuli. 289 female subjects aged between 22 to 37 responded to two of the eight photographs during September, 1991. 110 words were reduced to 62 words based on their independence before conducting factor analysis to identify the constructing factors of clothing images. Nine words with negative connotations were eliminated, because they are not sought in product development. To explain the hierarchy of clothing images, cluster analysis was applied. To observe the association of 53 words, dendrogram was introduced, and to interpret the result, eleven sub clusters were determined. This 11 clusters were continuously combined according to their similarities, until they integrated into one 'clothing image'. Two major division of image clusters were 'graceful and feminine image', and 'mannish and simple image'.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.116-120
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• 2008

Clustering is a method for unsupervised learning. General clustering tools have been depended on statistical methods and machine learning algorithms. One of the popular clustering algorithms based on machine learning is the self organizing map(SOM). SOM is a neural networks model for clustering. SOM and extended SOM have been used in diverse classification and clustering fields such as data mining. But, SOM has had a problem determining optimal number of clusters. In this paper, we propose an improvement of SOM using gap statistic and probability distribution. The gap statistic was introduced to estimate the number of clusters in a dataset. We use gap statistic for settling the problem of SOM. Also, in our research, weights of feature nodes are updated by probability distribution. After complete updating according to prior and posterior distributions, the weights of SOM have probability distributions for optima clustering. To verify improved performance of our work, we make experiments compared with other learning algorithms using simulation data sets.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.25-30
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• 2018

Pattern classification system is often an important component of intelligent systems. In this paper, we present a pattern classification system consisted of the feature selection module, knowledge base construction module and decision module. We introduce a feature impact evaluation selection method based on fuzzy cluster analysis considering computational approach and generalization capability of given data characteristics. A fuzzy neural network, OFUN-NET based on unsupervised learning data mining technique produces knowledge base for representative clusters. 240 blemish pattern images are prepared and applied to the proposed system. Experimental results show the feasibility of the proposed classification system as an automating defect inspection tool.

• Journal of the Korean Home Economics Association
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• v.30 no.3
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• pp.56-62
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• 1992

The purpose of this study was to characterize the somatotype of children and to provide fundamental data for the construction of the dress figure. The subjects for anthropometric measurements were 384 elementary school children aged from 6 to 7 living in Seoul and Pucheon. The data were analyzed statistically according to SPSS/PC ＋ version 3.1. Through the factor analysis, six factors were obtained. The six factors represented the body girth and weight, the height and sleeve length, the trunk length, the shoulder size, the body curvature, and the posture of upper torso, respectively. The subjects were classified into six clusters. Among the six clusters, four clusters covered about 95.9% of the whole subjects were determined as the sources of fundamental data for the children's dress figure.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.625-637
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• 2015

We develop a Bayesian clustering procedure based on a Dirichlet process prior with cluster specific random effects. Gibbs sampling of a normal mixture of linear mixed regressions with a Dirichlet process was implemented to calculate posterior probabilities when the number of clusters was unknown. Our approach (unlike its counterparts) provides simultaneous partitioning and parameter estimation with the computation of the classification probabilities. A Monte Carlo study of curve estimation results showed that the model was useful for function estimation. We find that the proposed Dirichlet process mixture model with cluster specific random effects detects clusters sensitively by combining vague edges into different clusters. Examples are given to show how these models perform on real data.

• Korean Journal of Remote Sensing
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• v.22 no.2
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• pp.111-121
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• 2006

The classification using spectral angle is a new approach based on the fact that the spectra of the same type of surface objects in RS data are approximately linearly scaled variations of one another due to atmospheric and topographic effects. There are many researches on the unsupervised classification using spectral angle recently. Nevertheless, there are only a few which consider the characteristics of Hyperspectral data. On this study, we propose the ISOMUSAC(Iterative Self-Organizing Modified Unsupervised Spectral Angle Classification) which can supplement the defects of previous unsupervised spectral angle classification. ISOMUSAC uses the Angle Division for the selection of seed points and calculates the center of clusters using spectral angle. In addition, ISOMUSAC perform the iterative merging and splitting clusters. As a result, the proposed algorithm can reduce the time of processing and generate better classification result than previous unsupervised classification algorithms by visual and quantitative analysis. For the comparison with previous unsupervised spectral angle classification by quantitative analysis, we propose Validity Index using spectral angle.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.23.4-23
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• 2001

This paper proposes a new approach to unsupervised classification of remotely sensed images. Fusion of optic images (Landsat TM) and radar data (SAR) has beer used to increase the accuracy of classification. Number of clusters is estimated using generalized Dunns measure. Performance of the proposed method is best observed comparing the classified images with classified aerial images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2191-2201
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• 2012

Traffic classification seeks to assign packet flows to an appropriate quality of service (QoS) class based on flow statistics without the need to examine packet payloads. Classification proceeds in two steps. Classification rules are first built by analyzing traffic traces, and then the classification rules are evaluated using test data. In this paper, we use self-organizing map and K-means clustering as unsupervised machine learning methods to identify the inherent classes in traffic traces. Three clusters were discovered, corresponding to transactional, bulk data transfer, and interactive applications. The K-nearest neighbor classifier was found to be highly accurate for the traffic data and significantly better compared to a minimum mean distance classifier.