• KOREAN JOURNAL OF CROP SCIENCE
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• v.28 no.3
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• pp.340-344
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• 1983

Taxonomic distances and Q correlations of all possible comparisons among thirty-two collected soybean varieties were calculated from the standardized mean of twenty-one characters. Ten varietal groups were classified by the single linkage clustering based on Q correlations. The means of Q correlations of intra-group were higher than those of inter-group. Each groups were characteristic in each mean of characters within varietal groups.

• Journal of The Korean Astronomical Society
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• v.29 no.spc1
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• pp.19-21
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• 1996

We apply topological measures of clustering such as percolation and genus curves (PC & GC) and shape statistics to a set of scale free N-body simulations of large scale structure. Both genus and percolation curves evolve with time reflecting growth of non-Gaussianity in the N-body density field. The amplitude of the genus curve decreases with epoch due to non-linear mode coupling, the decrease being more noticeable for spectra with small scale power. Plotted against the filling factor GC shows very little evolution - a surprising result, since the percolation curve shows significant evolution for the same data. Our results indicate that both PC and GC could be used to discriminate between rival models of structure formation and the analysis of CMB maps. Using shape sensitive statistics we find that there is a strong tendency for objects in our simulations to be filament-like, the degree of filamentarity increasing with epoch.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.3
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• pp.210-216
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• 2006

In the rule based modeling, data partitioning plays crucial role be cause partitioned sub data set implies particular information of the given data set or system. In this paper, we present an empirical study result of the data pattern estimation to find underlying data patterns of the given data. Presented method performs crisp type clustering with given n number of data samples by means of the sequential agglomerative hierarchical nested model (SAHN). In each sequence, the average value of the sum of all inter-distance between centroid and data point. In the sequel, compute the derivation of the weighted average distance to observe a pattern distribution. For the final step, after overall clustering process is completed, weighted average distance value is applied to estimate range of the number of clusters in given dataset. The proposed estimation method and its result are considered with the use of FCM demo data set in MATLAB fuzzy logic toolbox and Box and Jenkins's gas furnace data.

• The KIPS Transactions:PartD
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• v.10D no.7
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• pp.1093-1102
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• 2003

As the use of internet is growing, the amount of information is increasing rapidly and XML that is a standard of the web data has the property of flexibility of data representation. Therefore electronic document systems based on web, such as EDMS (Electronic Document Management System), ebXML (e-business extensible Markup Language), have been adopting XML as the method for exchange and standard of documents. So research on the method which can manage and search structural XML documents in an effective wav is required. In this paper we propose the clustering method based on structural similarity among the many XML documents, using typical structures extracted from each document by sequential pattern mining in pre-clustering process. The proposed algorithm improves the accuracy of clustering by computing cost considering cluster cohesion and inter-cluster similarity.

• Journal of IKEEE
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• v.25 no.4
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• pp.679-688
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• 2021

Gait is an evaluation index used in various clinical area including brain nervous system diseases. Signal source localizing and time-frequency analysis are mainly used after extracting independent components for Electroencephalogram data as a method of measuring and analyzing brain activation related to gait. Existing treadmill-based walking EEG analysis performs signal preprocessing, independent component analysis(ICA), and source localizing by merging data after the multiple EEG measurements, and extracts representative component clusters through inter-subject clustering. In this study we propose an analysis method, without merging to single dataset, that performs signal preprocessing, ICA, and source localization on each measurements, and inter-subject clustering is conducted for ICs extracted from all subjects. The effect of data merging on the IC clustering and time-frequency analysis was investigated for the proposed method and two conventional methods. As a result, it was confirmed that a more subdivided gait-related brain signal component was derived from the proposed "non-merging" method (4 clusters) despite the small number of subjects, than conventional method (2 clusters).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3295-3311
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• 2020

With the wide application of hyperspectral images, it becomes more and more important to compress hyperspectral images. Conventional recursive least squares (CRLS) algorithm has great potentiality in lossless compression for hyperspectral images. The prediction accuracy of CRLS is closely related to the correlations between the reference bands and the current band, and the similarity between pixels in prediction context. According to this characteristic, we present an improved CRLS with adaptive band selection and adaptive predictor selection (CRLS-ABS-APS). Firstly, a spectral vector correlation coefficient-based k-means clustering algorithm is employed to generate clustering map. Afterwards, an adaptive band selection strategy based on inter-spectral correlation coefficient is adopted to select the reference bands for each band. Then, an adaptive predictor selection strategy based on clustering map is adopted to select the optimal CRLS predictor for each pixel. In addition, a double snake scan mode is used to further improve the similarity of prediction context, and a recursive average estimation method is used to accelerate the local average calculation. Finally, the prediction residuals are entropy encoded by arithmetic encoder. Experiments on the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) 2006 data set show that the CRLS-ABS-APS achieves average bit rates of 3.28 bpp, 5.55 bpp and 2.39 bpp on the three subsets, respectively. The results indicate that the CRLS-ABS-APS effectively improves the compression effect with lower computation complexity, and outperforms to the current state-of-the-art methods.

• Journal of Communications and Networks
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• v.13 no.6
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• pp.664-677
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• 2011

In order to control interference and improve spectrum efficiency in the femtocell and macrocell overlaid system (FMOS), we propose a joint frequency bandwidth dynamic division, clustering and power control algorithm (JFCPA) for orthogonal-frequency-division-multiple access-based downlink FMOS. The overall system bandwidth is divided into three bands, and the macro-cellular coverage is divided into two areas according to the intensity of the interference from the macro base station to the femtocells, which are dynamically determined by using the JFCPA. A cluster is taken as the unit for frequency reuse among femtocells. We map the problem of clustering to the MAX k-CUT problem with the aim of eliminating the inter-femtocell collision interference, which is solved by a graph-based heuristic algorithm. Frequency bandwidth sharing or splitting between the femtocell tier and the macrocell tier is determined by a step-migration-algorithm-based power control. Simulations conducted to demonstrate the effectiveness of our proposed algorithm showed the frequency-reuse probability of the FMOS reuse band above 97.6% and at least 70% of the frequency bandwidth available for the macrocell tier, which means that the co-tier and the cross-tier interference were effectively controlled. Thus, high spectrum efficiency was achieved. The simulation results also clarified that the planning of frequency resource allocation in FMOS should take into account both the spatial density of femtocells and the interference suffered by them. Statistical results from our simulations also provide guidelines for actual FMOS planning.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10B
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• pp.964-972
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• 2009

Wireless mesh network (WMN) is emerging a future core technology to resolve many problems derived from exist wireless networks by employing multi-interface and multi-channel. Ability to utilize multiple channels in WMNs substantially increases the effective bandwidth available to wireless network nodes. However, minimum interference channel assignment algorithms are required to use the effective bandwidth in multi-channel environments. This paper proposes a cluster-based minimum interference channel assignment (MI-CA) algorithm to improve the performance of WMN. The MI-CA algorithm is consists of Inter-Cluster and Intra-Cluster Intrchannel assignment between clusters and in the internal clusters, respectively. The Inter-Cluster channel assignment assigns a barebone channel to cluster heads and border nodes based on minimum spanning tree (MST) and the Intra-Cluster channel assignment minimizes channel interference by reassigning ortasgonal channels between cluster mespann. Our simheation results show that MI-CA can improve the performance of WMNs by minimizing channel interference.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.11
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• pp.1060-1073
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• 2013

In this paper, we present a cluster-based routing scheme for efficiently delivering data to group mobile users by extracting and clustering mobile user group simply from beacon message information in wireless ad-hoc networks. First, we propose an online-clustering mechanism that uses a local neighbor table on each node by recursively transmitting to neighbor nodes, and forms a group table where a set of listed nodes are classified as group members, without incurring much overhead. A node that appears the most frequently from neighbor tables throughout the network is selected as the cluster-head node, serving as a data gateway for the intra-cluster. Second, we design an inter-cluster routing that delivers data from stationary data sources to the selected cluster-head node, and a intra-cluster routing to deliver from the cluster-head node to users. Simulation results based on ns-2 in the ad-hoc networks consisting of 518 stationary nodes and 20 mobile nodes show that our proposed clustering mechanism achieves high clustering accuracy of 96 % on average. Regarding routing performance, our cluster-based routing scheme outperforms a naive one-to-one routing scheme without any clustering by reducing routing cost up to 1/20. Also, our intra-cluster routing utilizing a selected cluster-head node reduces routing cost in half as opposed to a counterpart of the intra-cluster routing through a randomly-selected internal group member.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.6B
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• pp.342-349
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• 2007

Clustering protocol of Wireless sensor networks(WSNs) not only reducing the volume of inter-node communication by the nodes's data aggreation but also extending the nodes's sleep times by cluster head's TDMA-schedule coordination. In order to extend network lifetime of WSNs, we propose ECS algorithm to select cluster-head using three variables. It consists of initial and current energy of nodes, round information and total numbers which have been selected as cluster head until current round.