• Communications for Statistical Applications and Methods
• /
• v.10 no.3
• /
• pp.1057-1068
• /
• 2003

Results of using principal component analysis prior to cluster analysis are compared with results from applying agglomerative clustering algorithm alone. The retrieval ability of the agglomerative clustering algorithm is improved by using principal components prior to cluster analysis in some situations. On the other hand, the loss in retrieval ability for the agglomerative clustering algorithms decreases, as the number of informative variables increases, where the informative variables are the variables that have distinct information(or, necessary information) compared to other variables.

• Communications for Statistical Applications and Methods
• /
• v.10 no.2
• /
• pp.387-397
• /
• 2003

Generally, most of researches that need a variable-clustering process use an exploratory factor analysis technique or a divisive hierarchical variable-clustering method based on a correlation matrix. And some researchers apply a object-clustering method to a distance matrix transformed from a correlation matrix, though this approach is known to be improper. On this paper an agglomerative hierarchical variable-clustering method based on a correlation matrix itself is suggested. It is derived from a geometric concept by using variate-spaces and a characterizing variate.

• Proceedings of the KSRS Conference
• /
• 2008.10a
• /
• pp.406-409
• /
• 2008

Clustering data streams has an importance over many applications like sensor networks. Existing hierarchical methods follow a semi fuzzy clustering that yields duplicate clusters. In order to solve the problems, we propose an extended online divisive agglomerative clustering on data streams. It builds a tree-like top-down hierarchy of clusters that evolves with data streams using geometric time frame for snapshots. It is an enhancement of the Online Divisive Agglomerative Clustering (ODAC) with a pruning strategy to avoid duplicate clusters. Our main features are providing update time and memory space which is independent of the number of examples on data streams. It can be utilized for clustering sensor data and network monitoring as well as web click streams.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.7
• /
• pp.579-583
• /
• 2001

In the area of data clustering in high dimensional space, one of the difficulties is the time-consuming process for computing vector similarities. It becomes worse in the case of the agglomerative algorithm with the group-average link and mean centroid method, because the cluster similarity must be recomputed whenever the cluster center moves after the merging step. As a solution of this problem, we present an incremental method of similarity computation, which substitutes the scalar calculation for the time-consuming calculation of vector similarity with several measures such as the squared distance, inner product, cosine, and minimum variance. Experimental results show that it makes clustering speed significantly fast for very high dimensional data.

• Proceedings of the IEEK Conference
• /
• 2003.11a
• /
• pp.377-380
• /
• 2003

A new color image segmentation scheme is presented in this paper. The proposed algorithm consists of image simplification, region labeling and color clustering. The vector-valued diffusion process is performed in the perceptually uniform LUV color space. We present a discrete 3-D diffusion model for easy implementation. The statistical characteristics of each labeled region are employed to estimate the number of total clusters and agglomerative hierarchical clustering is performed with the estimated number of clusters. Since the proposed clustering algorithm counts each region as a unit, it does not generate oversegmentation along region boundaries.

• Journal of the Korean Society for Library and Information Science
• /
• v.38 no.4
• /
• pp.179-192
• /
• 2004

In this paper, we present that the result of document clustering can change dramatically with respect to the different ways of indexing compound nouns. First of all, the automatic indexing engine specialized for Korean words analysis, which also serves as the backbone engine for automatic document clustering system, is introduced. Then, the details of hierarchical agglomerative clustering(HAC) method, one of the widely used clustering methodologies in these days, was illustrated. As the result of observing the experiments, carried out in the final part of this paper, it comes to the conclusion that the various modes of indexing compound nouns have an effect on the outcome of HAC.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.96-96
• /
• 2020

In order to establish the risk criteria of inundation due to typhoons or heavy rainfall, research is underway to predict the limit rainfall using basin characteristics, limit rainfall and artificial intelligence algorithms. In order to improve the model performance in estimating the limit rainfall, the learning data are used after the pre-processing. When 50.0% of the entire data was removed as an outlier in the pre-processing process, it was confirmed that the accuracy is over 90%. However, the use rate of learning data is very low, so there is a limitation that various characteristics cannot be considered. Accordingly, in order to predict the limit rainfall reflecting various watershed characteristics by increasing the use rate of learning data, the watersheds with similar characteristics were clustered. The algorithms used for clustering are K-Means, Agglomerative, DBSCAN and Spectral Clustering. The k-Means, DBSCAN and Agglomerative clustering algorithms are clustered at the impervious area ratio, and the Spectral clustering algorithm is clustered in various forms depending on the parameters. If the results of the clustering algorithm are applied to the limit rainfall prediction algorithm, various watershed characteristics will be considered, and at the same time, the performance of predicting the limit rainfall will be improved.

• ETRI Journal
• /
• v.40 no.2
• /
• pp.227-236
• /
• 2018

Ultra-dense small cell networks (UD-SCNs) have been identified as a promising scheme for next-generation wireless networks capable of meeting the ever-increasing demand for higher transmission rates and better quality of service. However, UD-SCNs will inevitably suffer from severe interference among the small cell base stations, which will lower their spectral efficiency. In this paper, we propose a software-defined networking (SDN)-based hierarchical agglomerative clustering (SDN-HAC) framework, which leverages SDN to centrally control all sub-channels in the network, and decides on cluster merging using a similarity criterion based on a suitability function. We evaluate the proposed algorithm through simulation. The obtained results show that the proposed algorithm performs well and improves system payoff by 18.19% and 436.34% when compared with the traditional network architecture algorithms and non-cooperative scenarios, respectively.

• Journal of Advanced Information Technology and Convergence
• /
• v.8 no.2
• /
• pp.89-99
• /
• 2018

Due to global warming, various natural disasters such as floods and droughts are increasing. If we can detect the possibility of natural disasters in advance, we can prevent massive damages caused by natural disasters. Recent advances in visual sensor technologies have enabled remote monitoring of a variety of natural environments, including lakes, rivers, and shores. In this paper, we propose a method to segment an image obtained from video sensor networks into regions in order to monitor the environment effectively. In the proposed method, we first partition the image into superpixels and model the connections between superpixels as a graph. Then, initial seeds for each region are set by using the prior information, and the initial seeds are expanded to form regions using agglomerative clustering. Experimental results show that the proposed method extracts the regions from natural environment images easily and accurately.

• Journal of Korean Society for Quality Management
• /
• v.18 no.1
• /
• pp.48-54
• /
• 1990

The purpose of this study is to design effective working systems which adapt to change in human needs by developing an method which forms into optimal groups using the measure of cohesion. Two main results can be derived from the study as follows : First, the clustering method based on the entropic measure of cohesion is predominant with respect to any other methods proposed in designing the work groups, since this clustering criterion includes symmetrical relations of total work groups and the dissimilarity as well as the similarity relations of predicate value, the clustering method based on this criterion is suitable for designing the new work structure. Second, total work group is clustered as the workers who have the equal predicate value and then clustering results are produced through the combinatorial agglomerative hierarchical clustering method. This clustering method present more economic results than the method that clustering the total work group do.