• Journal of Korea Multimedia Society
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• v.17 no.10
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• pp.1160-1170
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• 2014

Clustering methods are very useful in many fields such as data mining, classification, and object recognition. Both the supervised and unsupervised grouping approaches can classify a series of sample data with a predefined or automatically assigned cluster number. However, there is no constraint on the number of elements for each cluster. Numbers of cluster members for each cluster obtained from clustering schemes are usually random. Thus, some clusters possess a large number of elements whereas others only have a few members. In some areas such as logistics management, a fixed number of members are preferred for each cluster or logistic center. Consequently, it is necessary to design a clustering method that can automatically adjust the number of group elements. In this paper, a k-means based clustering method with a fixed number of cluster members is proposed. In the proposed method, first, the data samples are clustered using the k-means algorithm. Then, the number of group elements is adjusted by employing a greedy strategy. Experimental results demonstrate that the proposed clustering scheme can classify data samples efficiently for a fixed number of cluster members.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.831-834
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• 2011

LEACH(One of the leading algorithms in the field of WSN) for the life of the system, even by the number of all nodes to ensure that the cluster header. However, each round does not guarantee a certain number of cluster header. So sometimes cluster header is elected of small number or not elected. If cluster header number is to small, takes a heavy load on cluster header. And empty cluster is occur depending on the location of the cluster header. The algorithm proposed in this paper, the area of interest is divided into sectors. And randomly, cluster header be elected one the in each sector. When clustering the sensor nodes will belong to the nearest cluster header. So clustering is independent of the sector. This algorithm is guarantee a certain number of cluster header in each round. And has prevent occurrence of empty cluster.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.2
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• pp.7-14
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• 2016

In this paper presents a low power algorithm using a feasible cluster generation method considered glitch. The proposed algorithm is a method for reducing power consumption of a given circuit. The algorithm consists of a feasible cluster generation process and glitches removal process. So that glitches are not generated for the node to which the switching operation occurs most frequently in order to reduce the power consumption is a method for generating a feasible cluster. A feasible cluster generation process consisted of a node value set, dividing the node, the node aligned with the feasible cluster generation. A feasible cluster generation procedure is produced from the highest number of nodes in the output. When exceeding the number of OR-terms of the inputs of the selected node CLB prevents the signal path is varied by the evenly divided. If there are nodes with the same number of outputs selected by the first highest number of nodes in the input produces a feasible cluster. Glitch removal process removes glitches through the path balancing in the same manner as [5]. Experimental results were compared with the proposed algorithm [5]. Number of blocks has been increased by 5%, the power consumption was reduced by 3%.

• 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.

• Communications for Statistical Applications and Methods
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• v.23 no.1
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• pp.47-55
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• 2016

Stratified cluster sampling has been widely used for effective parameter estimations due to reductions in time and cost. The probability proportional to size (PPS) sampling method is used when the number of cluster element are significantly different. However, simple random sampling (SRS) is commonly used for simplicity if the number of cluster elements are almost the same. Also it is known that the ratio estimator produces a good performance when the total number of population elements is known. However, the two stage cluster estimator should be used if the total number of elements in population is neither known nor accurate. In this study we suggest a composite estimator by combining the ratio estimator and the two stage cluster estimator to obtain a better estimate under a certain population circumstance. Simulation studies are conducted to compare the superiority of the suggested estimator with two other estimators.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.6
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• pp.621-626
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• 1999

In this paper a new cluster validation index which is heuristic but able to eliminate the monotonically decreasing tendency occurring in which the number of cluster c gets very large and close to the number of data points n is proposed. We review the FCM algorithm and some conventional cluster validity criteria discuss on the limiting behavior of the proposed validity index and provide some numerical examples showing the effectiveness of the proposed cluster validity index.

• 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.

• 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.

• Journal of Korea Multimedia Society
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• v.21 no.6
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• pp.667-677
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• 2018

Since sensor networks consist of sensor nodes with limited energy resources, so efficient energy use of sensor nodes is very important in the design of sensor networks. Sensor nodes consume a lot of energy for data transmission. Clustering technique is used to efficiently use energy in data transmission. Recently, mobile sink techniques have been proposed to reduce the energy load concentrated on the cluster header near a sink node. The CMS(Cluster-based Mobile sink) technique minimizes the generation of control messages by creating a data transmission path while creating clusters, and supports the inter-cluster one-hop transmission. But, there is a case where there is no connectivity between neighbor clusters, it causes a problem of having a long hop data transmission path regardless of local distance. In this paper, we propose a SCBC(Strong connection balancing cluster) to support the path of the minimum number of hops. The proposed scheme minimizes the number of hops in the data transmission path and supports efficient use of energy in the cluster header. This also minimizes a number of hops in data transmission paths even when the sink moves and establishes a new path, and it supports the effect of extending the life cycle of the entire sensor network.

• ETRI Journal
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• v.41 no.5
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• pp.648-657
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• 2019

A cluster topology was proposed with the assumption of zero noise to improve the performance of wireless body area networks (WBANs). However, in WBANs, the transmission power should be reduced as low as possible to avoid the effect of electromagnetic waves on the human body and to extend the lifetime of a battery. Therefore, in this work, we consider a bit error rate for a cluster-based WBAN and analyze the performance of the system while the transmission of sensors and cluster headers (CHs) is controlled. Moreover, a hierarchical topology is proposed for the cluster-based WBAN to further improve the throughput of the system; this proposed system is called as the hierarchical cluster WBAN. The hierarchical cluster WBAN is combined with a transmission control scheme, that is, complete control, spatial reuse superframe, to increase the throughput. The proposed system is analyzed and evaluated based on several factors of the system model, such as signal-to-noise ratio, number of clusters, and number of sensors. The calculation result indicates that the proposed hierarchical cluster WBAN outperforms the cluster-based WBAN in all analyzed scenarios.