• Communications for Statistical Applications and Methods
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• v.12 no.1
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• pp.207-216
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• 2005

A binary segmentation procedure is a simple recursive approach to detect clusters and provide inferences for the study space when the shape of the clusters and the number of clusters are unknown. The procedure involves a sequence of nested hypothesis tests of a single cluster versus a pair of distinct clusters. The size and the shape of the clusters evolve as the procedure proceeds. The procedure allows for various growth clusters and for arbitrary baseline densities which govern the form of the hypothesis tests. A real tree data is used to highlight the procedure.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.2
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• pp.44-57
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• 1995

In this paper GFI (Generalized Fuzzy Isodata) and FI (Fuzzy Isodata) algorithms are studied and applied to the tire tread pattern classification problem. GFI algorithm which repeatedly grouping the partitioned cluster depending on the fuzzy partition matrix is general form of GI algorithm. In the constructing the binary tree using GFI algorithm cluster validity, namely, whether partitioned cluster is feasible or not is checked and construction of the binary tree is obtained by FDH clustering algorithm. These algorithms show the good performance in selecting the prototypes of each patterns and classifying patterns. Directions of edge in the preprocessed image of tire tread pattern are selected as features of pattern. These features are thought to have useful information which well represents the characteristics of patterns.

• Journal of The Korean Astronomical Society
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• v.38 no.4
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• pp.415-422
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• 2005

UBVIJHK photometry of the open cluster NGC 2194 are presented. Color-Magnitude diagrams of this cluster show well-defined main sequence and red giant clump. The main sequence also contains clear evidence of binary populations. Based on color-color diagrams, absolute magnitude of red giant clump, ZAMS fitting, and comparisons of observed color-magnitude diagrams with theoretical models, we derive following parameters for the cluster; reddening $E(B-V)=0.44{\pm}0.04$, age of log $t{\sim}8.8$, and finally distance of $(m-M)_0=12.20{\pm}0.18$.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.6
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• pp.102-110
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• 1996

A new design scheme of a binary decision tree is proposed. In this scheme a binary decision tree is constructed by using genetic algorithm and FCM algorithm. At each node optimal or near-optimal feature subset is selected which optimizes fitness function in genetic algorithm. The fitness function is inversely proportional to classification error, balance between cluster, number of feature used. The binary strings in genetic algorithm determine the feature subset and classification results - error, balance - form fuzzy partition matrix affect reproduction of next genratin. The proposed design scheme is applied to the tire tread patterns and handwriteen alphabetic characters. Experimental results show the usefulness of the proposed scheme.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.7 no.2
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• pp.219-226
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• 1996

Basic objective in cluster analysis is to discover natural groupings of items or variables. In general, variable clustering was conducted based on some similarity measures between variables which have binary characteristics. We propose a variable clustering method when variables have more categories ordered in some sense. We also consider some measures of association as a similarity between variables. Numerical example is included.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.2
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• pp.38.1-38.1
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• 2018

A merging galaxy cluster is a useful laboratory to study many interesting astrophysical processes such as intracluster medium heating, particle acceleration, and possibly dark matter self-interaction. However, without understanding the merger scenario of the system, interpretation of the observational data is severely limited. In this work, we focus on the off-axis binary cluster merger Abell 115, which possesses many remarkable features. The cluster has two cool cores in X-ray with disturbed morphologies and a single giant radio relic just north of the northern X-ray peak. In addition, there is a large discrepancy (almost a factor of 10) in mass estimate between weak lensing and dynamical analyses. To constrain the merger scenario, we perform a hydrodynamical simulation with the adaptive mesh refinement code RAMSES. We use the multi-wavelength observational data including X-ray, weak-lensing, radio, and optical spectroscopy to constrain the merger scenario. We present detailed comparisons between the simulation results and these multi-wavelength observations.

• Journal of The Korean Astronomical Society
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• v.44 no.2
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• pp.39-48
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• 2011

UBV I CCD photometry of NGC 2353 is performed as a part of the "Sejong Open cluster Survey" (SOS). Using photometric membership criteria we select probable members of the cluster. We derive the reddening and distance to the cluster, i.e., E(B - V ) = 0.10 ${\pm}$ 0.02 mag and 1.17 ${\pm}$ 0.04 kpc, respectively. We find that the projected distribution of the probable members on the sky is elliptical in shape rather than circular. The age of the cluster is estimated to be log(age)=8.1 ${\pm}$ 0.1 in years, older than what was found in previous studies. The minimum value of binary fraction is estimated to be about 48 ${\pm}$ 5 percent from a Gaussian function fit to the distribution of the distance moduli of the photometric members. Finally, we also obtain the luminosity function and the initial mass function (IMF) of the probable cluster members. The slope of the IMF is ${\Gamma}=-1.3{\pm}0.2$.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.719-732
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• 2006

A set of clustering algorithms with proper weight on the formulation of distance which extend to mixed numeric and multiple binary values is presented. A simple matching and Jaccard coefficients are used to measure similarity between objects for multiple binary attributes. Similarities are converted to dissimilarities between i th and j th objects. The performance of clustering algorithms with balancing weight on different similarity measures is demonstrated. Our experiments show that clustering algorithms with application of proper weight give competitive recovery level when a set of data with mixed numeric and multiple binary attributes is clustered.

• 정보저장시스템학회:학술대회논문집
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• 2005.10a
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• pp.129-133
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• 2005

Data storage related with writing and retrieving requires high storage capacity, fast transfer rate and less access time. Today any data storage system cannot satisfy these conditions, however holographic data storage system can perform faster data transfer rate because it is a page oriented memory system using volume hologram in writing and retrieving data. System can be constructed without mechanical actuating part therefore fast data transfer rate and high storage capacity about $1Tb/cm^3$ can be realized. In this paper, to reduce errors of binary data stored in holographic data storage system, a new method for bit error reduction is suggested. First, find cluster centers using subtractive clustering algorithm then reduce intensities of pixels around cluster centers and fuzzy rules. Therefore, By using this error reduction method following results are obtained ; the effect of Inter Pixel Interference noise is decreased and the intensity profile of data page becomes uniform therefore the better data storage system can be constructed.