• 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.

• ETRI Journal
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• v.43 no.3
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• pp.427-435
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• 2021

Data filtering is an essential task for improving the energy efficiency of radiofrequency identification (RFID) networks. Among various energy-efficient approaches, clustering-based data filtering is considered to be the most effective solution because data from cluster members can be filtered at cluster heads before being sent to base stations. However, this approach quickly depletes the energy of cluster heads. Furthermore, most previous studies have assumed that readers are fixed and interrogate mobile tags in a workspace. However, there are several applications in which readers are mobile and interrogate fixed tags in a specific area. This article proposes a model for dynamic clustering-based data filtering (DCDF) in mobile RFID networks, where mobile readers are re-clustered periodically and the cluster head role is rotated among the members of each cluster. Simulation results show that DCDF is effective in terms of balancing energy consumption among readers and prolonging the lifetime of the mobile RFID networks.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.233-237
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• 2006

This study proposes an innovative measure for evaluating the performance of text clustering. In using K-means algorithm and Kohonen Networks for text clustering, the number clusters is fixed initially by configuring it as their parameter, while in using single pass algorithm for text clustering, the number of clusters is not predictable. Using labeled documents, the result of text clustering using K-means algorithm or Kohonen Network is able to be evaluated by setting the number of clusters as the number of the given target categories, mapping each cluster to a target category, and using the evaluation measures of text. But in using single pass algorithm, if the number of clusters is different from the number of target categories, such measures are useless for evaluating the result of text clustering. This study proposes an evaluation measure of text clustering based on intra-cluster similarity and inter-cluster similarity, what is called CI (Clustering Index) in this article.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10b
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• pp.32-134
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• 1998

공간 색인의 하나인 그리드 파일은 공간 데이터 영역을 격자 형태의 셀로 분할하여 구성하는데 특히, 셀들의 크기가 모두 동일한 값으로 고정되어진 것을 고정 그리드(fixed grid)라고 한다. 셀들의 크기가 고정된으로 인해 샐 분할선 상에 객체가 존재하는 경우가 자주 발생하게 되고 이러한 객체들은 하나 이상의 셀에 의해 중복으로 참조된다. 중복 참조 객체는 1/10 시간을 증가시켜 질의 처리 시 성능 저하의 주요한 원인이 된다. 따라서 중복 객체를 효율적으로 처리 할 수 있는 클러스터링 알고리즘의 고안이 필요하다. 이 논문에서는 중복 참조 객체를 처리하기 위한 객체 클러스터링(Object clustering)과 셀 단위로 클러스터하기 위한 셀 클러스터링(Cell clustering) 알고리즘을 구현한다. 그리고 공간 질의 수행 시에 각 클러스터기법들에 대한 성능을 평가한다.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.471-483
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• 2012

One of the most important problems in cluster analysis is the selection of variables that truly define cluster structure, while eliminating noisy variables that mask such structure. Brusco and Cradit (2001) present VS-KM(variable-selection heuristic for K-means clustering) procedure for selecting true variables for K-means clustering based on adjusted Rand index. This procedure starts with the fixed number of clusters in K-means and adds variables sequentially based on an adjusted Rand index. This paper presents an updated procedure combining the VS-KM with the automated K-means procedure provided by Kim (2009). This automated variable selection procedure for K-means clustering calculates the cluster number and initial cluster center whenever new variable is added and adds a variable based on adjusted Rand index. Simulation result indicates that the proposed procedure is very effective at selecting true variables and at eliminating noisy variables. Implemented program using R can be obtained on the website "http://faculty.knou.ac.kr/sskim/nvarkm.r and vnvarkm.r".

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.381-386
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• 2004

This paper proposes an independent component analysis(ICA) of the fixed-point(FP) algorithm based on Newton method by adding the kurtosis. The kurtosis is applied for clustering the components, and the FP algorithm of Newton method is applied for improving the analysis speed and performance. The proposed ICA has been applied to the problems for separating the 6-mixed signals of 500 samples and 8-mixed images of $512\times512$pixels, respectively. The experimental results show that the proposed ICA has always a fixed analysis sequence. The result can be solved the limit of conventional ICA which has a variable sequence depending on the running of algorithm. Especially, the proposed ICA can be used to classify and identify the signals or the images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.5
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• pp.942-948
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• 2016

WSN based on wireless sensor nodes, Sensor nodes can not be reassigned and recharged if they once placed. Each sensor node comes into being involved to a communication network with its limited energy. But the existing proposed clustering techniques, being applied to WSN environment with irregular dispersion of sensor nodes, have the network reliability issues which bring about a communication interruption with the local node feature of unbalanced distribution in WSN. Therefore, the communications participation of the sensor nodes in the suggested algorithm is extended by 25% as the sensor field divided in the light of the non-uniformed distribution of sensor nodes and a static or a dynamic clustering algorithm adopted according to its partition of sensor node density in WSN. And the entire network life cycle was extended by 14% to ensure the reliability of the network.

• 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.

• Computers and Concrete
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• v.6 no.2
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• pp.155-165
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• 2009

Splitting tensile strength (STS) of high-performance concrete (HPC) is one of the important mechanical properties for structural design. This property is related to compressive strength (CS), water/binder (W/B) ratio and concrete age. This paper presents a clustering-based fuzzy model for the prediction of STS based on the CS and (W/B) at a fixed age (28 days). The data driven fuzzy model consists of three main steps: fuzzy clustering, inference system, and prediction. The system can be analyzed directly by the model from measured data. The performance evaluations showed that the fuzzy model is more accurate than the other prediction models concerned.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5437-5454
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• 2016

A Wireless Sensor Network (WSN) is composed of several sensor nodes which are severely restricted to energy and memory. Energy is the lifeblood of sensors and thus energy conservation is a critical necessity of WSN. This paper proposes a clustering algorithm namely Energy Efficient Trustworthy Clustering algorithm (EETCA), which focuses on three phases such as chief node election, chief node recycling process and bi-level trust computation. The chief node election is achieved by Dempster-Shafer theory based on trust. In the second phase, the selected chief node is recycled with respect to the current available energy. The final phase is concerned with the computation of bi-level trust, which is triggered for every time interval. This is to check the trustworthiness of the participating nodes. The nodes below the fixed trust threshold are blocked, so as to ensure trustworthiness. The system consumes lesser energy, as all the nodes behave normally and unwanted energy consumption is completely weeded out. The experimental results of EETCA are satisfactory in terms of reduced energy consumption and prolonged lifetime of the network.