• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.95-101
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• 2005

In this paper, we proposed a learning algorithm for the neuro-fuzzy modeling using a learning rule to adapt clustering. The proposed algorithm includes the data partition, assigning the rule into the process of partition, and optimizing the parameters using predetermined threshold value in self-constructing algorithm. In order to improve the clustering, the learning method of neuro-fuzzy model is extended and the learning scheme has been modified such that the learning of overall model is extended based on the error-derivative learning. The effect of the proposed method is presented using simulation compare with previous ones.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.1
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• pp.9-15
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• 2016

Source localization has developed in various fields of signal processing including radar, sonar, and wireless communication, etc. Source localization can be found by estimating the time difference of arrival between the each of sensors. Several methods like the NLS(Nonlinear Least Square) cost function have been proposed in order to improve the performance of time delay estimation. In this paper, we propose a clustering method using the four sensors with the same aperture as previous methods of using the three sensors. Clustering method can be improved the source localization performance by grouping similar estimated values. The performance of source localization using clustering method is evaluated by Monte Carlo simulation.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.531-538
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• 2005

In k-means clustering, we standardize variables before clustering and iterate two steps: units allocation by Euclidean sense and centroids updating. In applications to DB marketing where clusters are to be used as customer segments with similar consumption behaviors, we frequently acquire additional variables on the customers or the units through marketing campaigns a posteriori. Hence we need to modify the clusters originally formed after each campaign. The aim of this study is to propose a revision method of k-means clusters, incorporating added information by weighting clustering variables. We illustrate the proposed method in an empirical case.

• Journal of Korea Multimedia Society
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• v.17 no.5
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• pp.590-600
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• 2014

In the wireless sensor network environment, clustering scheme has a problem that a large amount of energy is unnecessarily consumed because of frequently occurred entire re-clustering process. Some of the studies were attempted to improve the network performance by getting rid of the entire network setup process. However, removing the setup process is not worthy. Because entire network setup relieves the burden of some sensor nodes. The primary aim of our scheme is to cut down the energy consumption through minimizing entire setup processes which occurred unnecessarily. Thus, we suggest a re-clustering scheme that considers event detection, transmitting energy, and the load on the nodes. According to the result of performance analysis, our scheme reduces energy consumption of nodes, prolongs the network lifetime, and shows higher data collection rate and higher data accuracy than the existing schemes.

• The KIPS Transactions:PartC
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• v.16C no.6
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• pp.783-790
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• 2009

The necessity of wireless sensor networks is increasing in the recent years. So many researches are studied in wireless sensor networks. The clustering algorithm provides an effective way to prolong the lifetime of the wireless sensor networks. The one-hop routing of LEACH algorithm is an inefficient way in the energy consumption of cluster-head, because it transmits a data to the BS(Base Station) with one-hop. On the other hand, other clustering algorithms transmit data to the BS with multi-hop, because the multi-hop transmission is an effective way. But the multi-hop routing of other clustering algorithms which transmits data to BS with multi-hop have a data bottleneck state problem. The unequal clustering algorithm solved a data bottleneck state problem by increasing the routing path. Most of the unequal clustering algorithms partition the nodes into clusters of unequal size, and clusters closer to the BS have small-size the those farther away from the BS. However, the energy consumption of cluster-head in unequal clustering algorithm is more increased than other clustering algorithms. In the thesis, I propose an energy efficient unequal clustering algorithm which decreases the energy consumption of cluster-head and solves the data bottleneck state problem. The basic idea is divided a three part. First of all I provide that the election of appropriate cluster-head. Next, I offer that the decision of cluster-size which consider the distance from the BS, the energy state of node and the number of neighborhood node. Finally, I provide that the election of assistant node which the transmit function substituted for cluster-head. As a result, the energy consumption of cluster-head is minimized, and the energy consumption of total network is minimized.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.3
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• pp.47-56
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• 2011

To apply Genetic algorithm toward document clustering, appropriate individual structure is required. Document clustering with the genetic algorithms (DCGA) uses the centroid vector type individual structure. New document clustering with the genetic algorithm (NDAGA) uses document allocated individual structure. In this paper, to find more suitable object structure and process for the document clustering, calculation, amount of calculation, run-time, and performance difference between the two methods were analyzed. In this paper, we have performed various experiments using both DCGA and NDCGA. Result of the experiment shows that compared to DCGA, NDCGA provided 15% faster execution time, about 5~10% better performance. This proves that the document allocated structure is more fitted than the centroid vector type structure when it comes to document clustering. In addition, NDCGA showed 15~25% better performance than the traditional clustering algorithms (K-means, Group Average).

• The Journal of the Acoustical Society of Korea
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• v.14 no.5
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• pp.5-10
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• 1995

In this paper we present a clustering procedure based on the use of HMM in order to get multiple statistical models which can well absorb the variants of each speaker with different ways of saying words. The HMM-clustered models obtained from the developed technique are applied to the speaker independent isolated word recognition. The HMM clustering method splits off all observation sequences with poor likelihood scores which fall below threshold from the training set and create a new model out of the observation sequences in the new cluster. Clustering is iterated by classifying each observation sequence as belonging to the cluster whose model has the maximum likelihood score. If any clutter has changed from the previous iteration the model in that cluster is reestimated by using the Baum-Welch reestimation procedure. Therefore, this method is more efficient than the conventional template-based clustering technique due to the integration capability of the clustering procedure and the parameter estimation. Experimental data show that the HMM-based clustering procedure leads to $1.43\%$ performance improvements over the conventional template-based clustering method and $2.08\%$ improvements over the single HMM method for the case of recognition of the isolated korean digits.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.890-893
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• 2008

자율성 및 이동성 갖는 네트워크 구조의 하나인 MANET(Mobile Ad-Hoc Networks)은 각 node들은 그 특성에 따라서 clustering service을 한다. node의 전송과정 중 path access에 대하여 중요성 또한 강조되고 있다. 일반적인 무선 네트워크 상에서의 node들은 clustering을 하게 되는데 그 과정에서 발생되는 여러 가지 문제점을 가지고 전송이 이루어진다. 모든 node들이 송, 수신상의 전송 범위(Beam forming area)가지고 있으며, 이러한 각 node들의 전송범위 내에 전송이 이루어지는 전통적인 전송기술 mechanism을 찾는다. 이러한 전송상황에서의 송신하는 node와 수신된 node간에 발생되고 있는 중복성의 문제점으로 즉, 상호적용에 의한 네트워크 duplicate(overlapping)이 크게 우려가 되고 있다. 이러한 전송상의 전송 범위 중첩, node간의 packet 간섭현상, packet의 중복수신 및 broadcasting의 storming현상이 나타난다. 따라서 본 논문에서는 상황정보의 속성을 이용한 계층적 상호 head node들의 접근된 위치와 연계되는 전송속도, 보존하고 있는 head node들의 에너지 source value, doppler효과를 통한 head node의 이동방향 등 분석한다. 분석된 방법으로 전송상의 계층적 path가 구성된 경험적 path 속성을 통한 네트워크 connectivity 신뢰성을 극대화 할 뿐만 아니라 네트워크의 전송 범위 duplicate을 사전에 줄일 수 있고 전송망의 최적화를 유지할 수 있는 기법의 하나인 상황정보를 이용한 ad hoc network의 ODDMRP(Ontology Doppler effect-based Dynamic Multicast Routing Protocol) clustering 기법을 제안한다.

• Journal of the Korean Data and Information Science Society
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• v.21 no.1
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• pp.121-128
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• 2010

K-means clustering is an iterative algorithm in which items are moved among sets of clusters until the desired set is reached. K-means clustering has been widely used in many applications, such as market research, pattern analysis or recognition, image processing, etc. It can identify dense and sparse regions among data attributes or object attributes. But k-means algorithm requires many hours to get k clusters that we want, because it is more primitive, explorative. In this paper we propose a new method of k-means clustering using a center of gravity for grid-based sample. It is more fast than any traditional clustering method and maintains its accuracy.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.6
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• pp.1230-1237
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• 2009

In this paper, we develop a design methodology of Granular-based Radial Basis Function Neural Networks(GRBFNN) by context-based clustering. In contrast with the plethora of existing approaches, here we promote a development strategy in which a topology of the network is predominantly based upon a collection of information granules formed on a basis of available experimental data. The output space is granulated making use of the K-Means clustering while the input space is clustered with the aid of a so-called context-based fuzzy clustering. The number of information granules produced for each context is adjusted so that we satisfy a certain reconstructability criterion that helps us minimize an error between the original data and the ones resulting from their reconstruction involving prototypes of the clusters and the corresponding membership values. In contrast to "standard" Radial Basis Function neural networks, the output neuron of the network exhibits a certain functional nature as its connections are realized as local linear whose location is determined by the values of the context and the prototypes in the input space. The other parameters of these local functions are subject to further parametric optimization. Numeric examples involve some low dimensional synthetic data and selected data coming from the Machine Learning repository.