• Journal of Korea Technology Innovation Society
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• v.5 no.2
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• pp.167-188
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• 2002

Summary: This paper analyzes Indian software industry in the perspective of innovation cluster. The research shows that the software industry has been following an upstream clustering process, where the major value activity is expanding from low value product/services to high value product/services. The growth of software industry could be successful because there was appropriate initial condition of Bangalore, such as the availability of high qualified human resources, excellent research institutes, small high-tech companies. The role of government was helpful for the late growth of software industry but not a critical factor for the initial development of the S/W cluster. It is suggested that government should consider the initial condition of a concerned location critically to implement a cluster-type innovation policy.

• 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.

• Journal of the Korean Society for information Management
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• v.21 no.4 s.54
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• pp.173-185
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• 2004

Fast and high-quality document clustering algorithms play an important role in providing data exploration by organizing large amounts of information into a small number of meaningful clusters. Many papers have shown that the hierarchical clustering method takes good-performance, but is limited because of its quadratic time complexity. In contrast, with a large number of variables, K-means has a time complexity that is linear in the number of documents, but is thought to produce inferior clusters. In this paper, Condor system using K-Means algorithm Compares with regular method that the initial centroids have been established in advance, our method performance has been improved a lot.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.12-17
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• 2003

Optimal determination of cluster size has an effect on the result of clustering. In K-means algorithm, the difference of clustering performance is large by initial K. But the initial cluster size is determined by prior knowledge or subjectivity in most clustering process. This subjective determination may not be optimal. In this Paper, the genetic algorithm based optimal determination approach of cluster size is proposed for automatic determination of cluster size and performance upgrading of its result. The initial population based on attribution is generated for searching optimal cluster size. The fitness value is defined the inverse of dissimilarity summation. So this is converged to upgraded total performance. The mutation operation is used for local minima problem. Finally, the re-sampling of bootstrapping is used for computational time cost.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.450-453
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• 2000

In this paper, an automated segmentation algorithm is proposed for MR brain images using T1-weighted, T2-weighted, and PD images complementarily. The proposed segmentation algorithm is composed of 3 steps. In the first step, cerebrum images are extracted by putting a cerebrum mask upon the three input images. In the second step, outstanding clusters that represent inner tissues of the cerebrum are chosen among 3-dimensional (3D) clusters. 3D clusters are determined by intersecting densely distributed parts of 2D histogram in the 3D space formed with three optimal scale images. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram. In the final step, cerebrum images are segmented using FCM algorithm with it’s initial centroid value as the outstanding cluster’s centroid value. The proposed segmentation algorithm complements the defect of FCM algorithm, being influenced upon initial centroid, by calculating cluster’s centroid accurately And also can get better segmentation results from the proposed segmentation algorithm with multi spectral analysis than the results of single spectral analysis.

• 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 ${\Gamma}=-1.3{\pm}0.2$.

• The Korean Journal of Franchise Management
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• v.7 no.1
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• pp.21-29
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• 2016

Purpose - The primary purpose of this study is to employ effective marketing methods using market segmentation of coffee shops by determining how motivations to visit coffee shops have different impacts on demographic profile of visitors and characteristics of coffee shop visits, so as to draw out a better understanding of customers of coffee market. Research design, data, and methodology - Data were collected using surveys of self-administered questionnaires toward coffee shop users in Daejeon, Korea. A number of samples used in data analysis were 253 excluding unusable responses. The data were analyzed through frequency, reliability, and factor analysis using SPSS 20.0. Factor analysis was conducted through the principal component analysis and varimax rotation method to derive factors of one or more eigen values. In addition, the cluster analysis, multivariate ANOVA, and cross-tab analysis were used for the market segmentation based on the types of motivation for coffee shop visits. The process of the cluster analysis is as follows. Four clusters were derived through hierarchical clustering, and k-means cluster analysis was then carried out using mean value of the four clusters as the initial seed value. Result - The factor analysis delineated four dimensions of motivation to visit coffee shops: ostentation motivation, hedonic motivation, esthetic motivation, utility motivation. The cluster analysis yielded four clusters: utility and esthetic seekers, hedonic seekers, utility seekers, ostentation seekers. In order to further specify the profile of four clusters, each cluster was cross tabulated with socio-demographics and characteristics of coffee shop visits. Four clusters are significantly different from each other by four types of motivations for coffee shop visits. Conclusions - This study has empirically examined the difference in demographic profile of visitors and characteristics of coffee shop visits by motivation to visit coffee shops. There are significant differences according to age, education background, marital status, occupation and monthly income. In addition, coffee shops use pattern characterization in frequency of visits to coffee shops, relationships with companion, purpose of visit, information sources, brand type, average expense per visit, important elements of selection attribute were significantly different depending on motivations for coffee shop visits.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.3B
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• pp.123-134
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• 2008

In this paper, a routing algorithm for wireless sensor networks is proposed to improve the efficiency of energy consumption in sensor nodes. Each sensor node has the value called ‘Flooding Level’ obtained through the initial flooding from a sink node instead of sending beacon messages in multi-hop sensor field. This value can be used for guaranteeing the sensor nodes to connect with a sink node and determining the roles of cluster-head and cluster-gateway node efficiently and simply during the clustering. If different algorithms are added to our protocol, it will work better in the side of energyefficiency. This algorithm is evaluated through analysis and extensive simulations.

• Journal of The Korean Astronomical Society
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• v.40 no.4
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• pp.157-160
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• 2007

I present a model to explain the mass segregation and shallow mass functions observed in the central parts of starburst stellar clusters. The model assumes that the initial pre-stellar cores mass function resulting from the turbulent fragmentation of the proto-cluster cloud is significantly altered by the cores coalescence before they collapse to form stars. With appropriate, yet realistic parameters, this model based on the competition between cores coalescence and collapse reproduces the mass spectra of the well studied Arches cluster. Namely, the slopes at the intermediate and high mass ends, as well as the peculiar bump observed at $6M_{\bigodot}$. This coalescence-collapse process occurs on a short timescale of the order of the free fall time of the proto-cluster cloud (i.e., a few $10^4$ years), suggesting that mass segregation in Arches and similar clusters is primordial. The best fitting model implies the total mass of the Arches cluster is $1.45{\times}10^5M_{\bigodot}$, which is slightly higher than the often quoted, but completeness affected, observational value of a few $10^4M_{\bigodot}$. The model implies a star formation efficiency of ${\sim}30$ percent which implies that the Arches cluster is likely to a gravitationally bound system.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.185-194
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• 2020

Designing of a hierarchical clustering algorithm is one of the numerous approaches to minimize the energy consumption of the Wireless Sensor Networks (WSNs). In this paper, a homogeneous and randomly deployed sensor nodes is considered. These sensors are energy constrained elements. The nominal selection of the Cluster Head (CH) which falls under the clustering part of the network protocol is studied and compared to Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. CHs in this proposed process is the function of total remaining energy of each node as well as total average energy of the whole arrangement. The algorithm considers initial energy, optimum value of cluster heads to elect the next group of cluster heads for the network as well as residual energy. Total remaining energy of each node is compared to total average energy of the system and if the result is positive, these nodes are eligible to become CH in the very next round. Analysis and numerical simulations quantify the efficiency and Average Energy Ratio (AER) of the proposed system.