• Journal of the Korean Data and Information Science Society
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• v.16 no.3
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• pp.557-566
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• 2005

Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. In this paper we used k-means clustering of several clustering techniques. The k-means Clustering Is classified as a partitional clustering method. We analyze 2002 Gyeongnam social indicator survey data using k-means clustering techniques for environmental information. We can use these outputs given by k-means clustering for environmental preservation and environmental improvement.

• 한국데이터정보과학회:학술대회논문집
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• 2004.10a
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• pp.77-86
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• 2004

Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. In this paper we used k-means clustering of several clustering techniques. The k-means Clustering is classified as a partitional clustering method. We analyze 2002 Gyeongnam social indicator survey data using k-means clustering techniques for environmental information. We can use these outputs given by k-means clustering for environmental preservation and environmental improvement.

• Journal of information and communication convergence engineering
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• v.6 no.4
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• pp.434-438
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• 2008

Massive data are continuously produced with a data rate of over several terabytes every day. These applications need effective clustering algorithms to achieve an overall high performance computation. In this paper, we propose ancestor as cluster center based approach to clustering, the K-means algorithm using ancestor. We modify the K-means algorithm. We present a clustering architecture and a clustering algorithm that minimize of I/Os and show a performance with excellent. In our experimental performance evaluation, we present that our algorithm can improve the I/O speed and the query processing time.

• Journal of the Korean Mathematical Society
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• v.60 no.3
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• pp.503-520
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• 2023

In this paper, using the theory of majorization, we discuss the Schur m power convexity for L-conjugate means of n variables and the Schur convexity for weighted L-conjugate means of n variables. As applications, we get several inequalities of general mean satisfying Schur convexity, and a few comparative inequalities about n variables Gini mean are established.

• Communications of Mathematical Education
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• v.19 no.4 s.24
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• pp.699-713
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• 2005

In this paper, we attempted to find out the meaning of several means and inequalities, their relationships and proposed the effective ways to teach them in college mathematics classes. That is, we introduced 8 proofs of arithmetic-geometric mean equality to explain the fact that there exist diverse ways of proof. The students learned the diverseproof-methods and applied them to other theorems and projects. From this, we found out that the attempt to develop the students' logical thinking ability by encouraging them to find out diverse solutions of a problem could be a very effective education method in college mathematics classes.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.1
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• pp.1-16
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• 1998

The zone clustering problem arising from several area such as deciding the optimal location of ambient measuring stations is to devide the 2-dimensional area into several sub areas in which included individual zone shows simimlar properties. In general, the optimal solution of this problem is very hard to obtain. Therefore, instead of finding an optimal solution, the generation of near optimal solution within the limited time is more meaningful. In this study, the combination of a genetic algorithm and the modified k-means method is used to obtain the near optimal solution. To exploit the genetic algorithm effectively, a representation of chromsomes and appropriate genetic operators are proposed. The k-means method which is originally devised to solve the object clustering problem is modified to improve the solutions obtained from the genetic algorithm. The experiment shows that the proposed method generates the near optimal solution efficiently.

• Journal of the Korean Society of Clothing and Textiles
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• v.22 no.4
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• pp.452-459
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• 1998

The purpose of this study was to discover a conceptual framework and evaluation process of apparel quality by means-end chain theory. The theoretical study was conducted to find out a conceptual framework and build a hypothetical evaluation process model of apparel quality. Apparel quality was perceived associative network called a means-end chain and was evaluated in several stages. A conceptual framework of apparel quality evaluation was organized into hierarchical relationships among four different dimensions: physical attribute, physical function, instrumental performance, and expressive performance. The means-end structure linked tangible physical attributes and function to more abstract instrumental and expressive performance. A hypothetical evaluation process model linked dimensions of apparel quality to the selected means-end relationship. Different consumers had different means-end chains for the same apparel. Therefore different subjects are likely to have different evaluation paths. From this study we can suggest an evaluation process model of apparel quality.

• Proceedings of the Safety Management and Science Conference
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• 2008.04a
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• pp.283-293
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• 2008

Analysis of Means(ANOM) is a visualization tool for comparing several means to the grand mean like control chart type. This paper reviews five ANOM methods for continuous data such as ANOM, ANOME (ANOM for Treatment Effects), ANCON (Analysis of Contrasts), ANOMV (ANOM for Variance), ANOMC (ANOM for Correaltion). Three ANOM tools for discrete data such as ANOMNP (ANOM for Nonconforming Proportions), ANOMNC (ANOM for Nonconforming Unit), ANOMNPU (ANOM for Nonconfirmities Per Unit) are also developed.

• Kyungpook Mathematical Journal
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• v.47 no.4
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• pp.595-600
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• 2007

In this paper we consider weighted arithmetic and geometric means of several positive definite operators proposed by Sagae and Tanabe and we establish a reverse inequality of the arithmetic and geometric means via Specht ratio and the Thompson metric on the convex cone of positive definite operators.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.196-201
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• 2007

The Fuzzy E-means (FCM) algorithm is a widely used clustering method that incorporates probabilitic memberships. Due to these memberships, it can be sensitive to noise data. In this paper, we propose a new fuzzy C-means clustering algorithm by incorporating the Parzen Window method to include density information of the data. Several experimental results show that our proposed density-based FCM algorithm outperforms conventional FCM especially for data with noise and it is not sensitive to initial cluster centers.