• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.792-796
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• 2009

Determining the optimal number of cluster is an important issue in research area of data clustering. It is choosing the cluster validity method and finding the cluster number where it optimizes the cluster validity. In this paper, an efficient heuristic for determining optimal number of cluster using clustering balance is proposed. The experimental results using k-means at artificial and real-life data set show that proposed algorithm is excellent in aspect of time efficiency.

• The Journal of the Korea Contents Association
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• v.9 no.9
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• pp.80-89
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• 2009

A fast and efficient search algorithm to determine an optimal number of clusters in clustering algorithms is presented. The method is based on cluster validity index which is a measure for clustering optimality. As the clustering procedure progresses and reaches an optimal cluster configuration, the cluster validity index is expected to be minimized or maximized. In this Paper, a fast non-exhaustive search method for finding the optimal number of clusters is designed and shown to work well in clustering. The proposed algorithm is implemented with the k-mean++ algorithm as underlying clustering techniques using CB and PBM as a cluster validity index. Experimental results show that the proposed method provides the computation time efficiency without loss of accuracy on several artificial and real-life data sets.

• International journal of advanced smart convergence
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• v.7 no.2
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• pp.33-37
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• 2018

In this study, we propose an iterative clustering algorithm that automatically clusters a set of voice signal data without a label into an optimal number of clusters and generates hmm model for each cluster. In the clustering process, the likelihood calculations of the clusters are performed using iterative hmm learning and testing while varying the number of clusters for given data, and the maximum likelihood estimation method is used to determine the optimal number of clusters. We tested the effectiveness of this clustering algorithm on a small-vocabulary digit clustering task by mapping the unsupervised decoded output of the optimal cluster to the ground-truth transcription, we found out that they were highly correlated.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.330-333
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• 2003

Initial cluster size for clustering of partitioning methods is very important to the clustering result. In K-means algorithm, the result of cluster analysis becomes different with optimal cluster size K. Usually, the initial cluster size is determined by prior and subjective information. Sometimes this may not be optimal. Now, more objective method is needed to solve this problem. In our research, we propose a hybrid genetic algorithm, a tree induction based evolution algorithm, for determination of optimal cluster size. Initial population of this algorithm is determined by the number of terminal nodes of tree induction. From the initial population based on decision tree, our optimal cluster size is generated. The fitness function of ours is defined an inverse of dissimilarity measure. And the bagging approach is used for saying computational time cost.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.12
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• pp.6081-6088
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• 2012

An unequal clustering method in wireless sensor networks is the technique that forms the cluster of different size. This method decreases whole energy consumption by solving the hot spot problem. In this paper, I propose a layer-based dynamic unequal clustering using the unequal clustering model. This method decreases whole energy consumption and maintain that equally using optimal cluster's number and cluster head position. I also show that proposed method is better than previous clustering method at the point of network lifetime.

• Journal of Korea Multimedia Society
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• v.11 no.12
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• pp.1706-1715
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• 2008

In a super-peer based P2P network, the network is clustered and each cluster is managed by a special peer, which is called a super-peer. A Super-peer has information of all the peers in its cluster. This type of clustered P2P model is known to have efficient information search and less traffic load than unclustered P2P model. In this paper, we compute the message traffic cost incurred by peers' query, join and update actions within a cluster as well as between the clusters. With these values, we estimate the optimal number of super-peers that minimizes the traffic cost for the various size of super-peer based P2P networks.

• Journal of Korea Multimedia Society
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• v.8 no.12
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• pp.1623-1630
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• 2005

Adaptive modulation and coding (AMC), which has a number of variation levels in accordance with the fading channel variation, is a promising technique for communication systems. In this paper, we present an AMC method using the cluster in OFDM system for bandwidth efficiency and performance improvement. The AMC schemes applied into each cluster or some clusters are determined by the minimum or the average SNR value among all the sub carriers within the corresponding cluster. It is important to find the optimal information on cluster because AMC performance can be varied according to the number and position of cluster. It is shown by computer simulation that the AMC method outperforms the fixed modulation in terms of bandwidth efficiency and its performance can be determined by the position and number of clusters.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3D
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• pp.347-356
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• 2009

This paper is described clustering analysis of traffic characteristics-based highway classification in order to deviate from methodologies of existing highway functional classification. This research focuses on comparing the clustering techniques performance based on the total within-group errors and deriving the optimal number of cluster. This research analyzed statistical clustering method (Hierarchical Ward's minimum-variance method, Nonhierarchical K-means method) and Kohonen self-organizing maps clustering method for highway characteristic classification. The outcomes of cluster techniques compared for the number of samples and traffic characteristics from subsets derived by the optimal number of cluster. As a comprehensive result, the k-means method is superior result to other methods less than 12. For a cluster of more than 20, Kohonen self-organizing maps is the best result in the cluster method. The main contribution of this research is expected to use important the basic road attribution information that produced the highway characteristic classification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.6
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• pp.1133-1151
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• 2010

Energy balanced consumption routing is based on assumption that the nodes consume energy both in transmitting and receiving. Lopsided energy consumption is an intrinsic problem in low-cost sensor networks characterized by multihop routing and in many traffic overhead pattern networks, and this irregular energy dissipation can significantly reduce network lifetime. In this paper, we study the problem of maximizing network lifetime through balancing energy consumption for uniformly deployed low-cost sensor networks. We formulate the energy consumption balancing problem as an optimal balancing data transmitting problem by combining the ideas of corona cluster based network division and optimized transmitting state routing strategy together with data transmission. We propose a localized cluster based routing scheme that guarantees balanced energy consumption among clusters within each corona. We develop a new energy cluster based routing protocol called "OECR". We design an offline centralized algorithm with time complexity O (log n) (n is the number of clusters) to solve the transmitting data distribution problem aimed at energy balancing consumption among nodes in different cluster. An approach for computing the optimal number of clusters to maximize the network lifetime is also presented. Based on the mathematical model, an optimized energy cluster routing (OECR) is designed and the solution for extending OEDR to low-cost sensor networks is also presented. Simulation results demonstrate that the proposed routing scheme significantly outperforms conventional energy routing schemes in terms of network lifetime.

• Journal of the Korean Operations Research and Management Science Society
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• v.42 no.3
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• pp.25-34
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• 2017

K-means is a popular and efficient data clustering method that only uses intra-cluster distance to establish a valid index with a previously fixed number of clusters. K-means is useless without a suitable number of clusters for unsupervised data. This paper aimsto propose the Group Search Optimization (GSO) using Silhouette to find the optimal data clustering solution with a number of clusters for unsupervised data. Silhouette can be used as valid index to decide the number of clusters and optimal solution by simultaneously considering intra- and inter-cluster distances. The performance of GSO using Silhouette is validated through several experiment and analysis of data sets.