• Journal of Information Technology Services
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• v.9 no.3
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• pp.95-106
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• 2010

Many researches have been performed to increase energy-efficiency in wireless sensor networks. One of primary research topics is about clustering protocols, which are adopted to configure sensor networks in the form of hierarchical structures by grouping sensor nodes into a cluster. However, legacy clustering protocols do not propose detailed methods from the perspective of implementation to determine a cluster's boundary and configure a cluster, and to communicate among clusters. Moreover, many of them involve assumptions inappropriate to apply those to a sensor field. In this paper, we have designed and implemented a new T-Clustering (Top-down Clustering) protocol, which takes into considerations a node's density, a distance between cluster heads, and remained energy of a node all together. Our proposal is a sink-node oriented top-down clustering protocol, and can form uniform clusters throughout the network. Further, it provides re-clustering functions according to the state of a network. In order to verify our protocol's feasibility, we have implemented and experimented T-Clustering protocol on Crossbow's MICAz nodes which are executed on TinyOS 2.0.2.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.8
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• pp.305-314
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• 2017

Clustering is a method that collects data objects into groups based on their similary. Performance of the state-of-the-art clustering methods is different according to the data characteristics. There have been numerous studies that performed experiments to compare the accuracy of the state-of-the-art clustering methods by applying various kinds of datasets. A common problem of these studies is that they only consider clustering algorithms that yield the most accurate results for a particular dataset. They do not consider what factors affect the execution time of each clustering method and how they are affected. Nevertheless, execution time is an important factor in clustering performance if there is no significant difference in accuracy. In order to solve the problems of the existing research, through a series of experiments using various types of datasets, we compare the accuracy of four representative clustering methods. In addition, we perform practical clustering performance comparisons by deriving time complexity and identifying factors that influences to its performance.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.661-671
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• 2005

The aim of this study is to propose a clustering method, called tree-structured clustering, by recursively partitioning continuous multivariate dat a based on overall $R^2$ criterion with a practical node-splitting decision rule. The clustering method produces easily interpretable clustering rules of tree types with the variable selection function. In numerical examples (Fisher's iris data and a Telecom case), we note several differences between tree-structured clustering and K-means clustering.

• Communications for Statistical Applications and Methods
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• v.31 no.5
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• pp.601-612
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• 2024

A functional clustering analysis is a crucial machine learning technique in functional data analysis. Many functional clustering methods have been developed to enhance clustering performance. Moreover, due to the phase variability between functions, elastic functional clustering methods, such as applying the Fisher-Rao metric, which can manage phase variation during clustering, have been developed to improve model performance. However, aligning functions without considering the phase variation can distort functional information because phase variation can be a natural characteristic of functions. Hence, we propose a state-of-the-art functional hierarchical clustering that can manage phase and amplitude variations of functional data. This approach is based on the phase and amplitude separation method using the norm-preserving time warping of functions. Due to its invariance property, this representation provides robust variability for phase and amplitude components of functions and improves clustering performance compared to conventional functional hierarchical clustering models. We demonstrate this framework using simulated and real data.

• Journal of the Korean Data and Information Science Society
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• v.16 no.3
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• pp.549-556
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• 2005

A hard-type centroids in the conventional clustering algorithm such as k-modes algorithm cannot keep the uncertainty inherently in data sets as long as possible before actual clustering(decision) are made. Therefore, we propose the k-populations algorithm to extend clustering ability and to heed the data characteristics. This k-population algorithm as found to give markedly better clustering results through various experiments.

• ETRI Journal
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• v.39 no.4
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• pp.443-454
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• 2017

In this paper, we propose a fine-grained mobile application clustering model using retrofitted document embedding. To automatically determine the clusters and their numbers with no predefined categories, the proposed model initializes the clusters based on title keywords and then merges similar clusters. For improved clustering performance, the proposed model distinguishes between an accurate clustering step with titles and an expansive clustering step with descriptions. During the accurate clustering step, an automatically tagged set is constructed as a result. This set is utilized to learn a high-performance document vector. During the expansive clustering step, more applications are then classified using this document vector. Experimental results showed that the purity of the proposed model increased by 0.19, and the entropy decreased by 1.18, compared with the K-means algorithm. In addition, the mean average precision improved by more than 0.09 in a comparison with a support vector machine classifier.

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

• Communications for Statistical Applications and Methods
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• v.22 no.4
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• pp.321-331
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• 2015

The K-means clustering algorithm is a popular and widely used method for clustering. For covariance matrices, we consider a geodesic clustering algorithm based on the K-means clustering framework in consideration of symmetric positive definite matrices as a Riemannian (non-Euclidean) manifold. This paper considers a geodesic clustering algorithm for data consisting of symmetric positive definite (SPD) matrices, utilizing the Riemannian geometric structure for SPD matrices and the idea of a K-means clustering algorithm. A K-means clustering algorithm is divided into two main steps for which we need a dissimilarity measure between two matrix data points and a way of computing centroids for observations in clusters. In order to use the Riemannian structure, we adopt the geodesic distance and the intrinsic mean for symmetric positive definite matrices. We demonstrate our proposed method through simulations as well as application to real financial data.

• Communications for Statistical Applications and Methods
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• v.27 no.6
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• pp.589-602
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• 2020

The development of smart grids has enabled the easy collection of a large amount of power data. There are some common patterns that make it useful to cluster power consumption patterns when analyzing s power big data. In this paper, clustering analysis is based on distance functions for time series and clustering algorithms to discover patterns for power consumption data. In clustering, we use 10 distance measures to find the clusters that consider the characteristics of time series data. A simulation study is done to compare the distance measures for clustering. Cluster validity measures are also calculated and compared such as error rate, similarity index, Dunn index and silhouette values. Real power consumption data are used for clustering, with five distance measures whose performances are better than others in the simulation.

• Communications for Statistical Applications and Methods
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• v.26 no.1
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• pp.57-67
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• 2019

Income distribution is a major concern in economic theory. In regional economics, it is often of interest to compare income distributions in different regions. Traditional methods often compare the income inequality of different regions by assuming parametric forms of the income distributions, or using summary statistics like the Gini coefficient. In this paper, we propose a nonparametric procedure to test for heterogeneity in income distributions among different regions, and a K-means clustering procedure for clustering income distributions based on energy distance. In simulation studies, it is shown that the energy distance based method has competitive results with other common methods in hypothesis testing, and the energy distance based clustering method performs well in the clustering problem. The proposed approaches are applied in analyzing data from China Health and Nutrition Survey 2011. The results indicate that there are significant differences among income distributions of the 12 provinces in the dataset. After applying a 4-means clustering algorithm, we obtained the clustering results of the income distributions in the 12 provinces.