• The Transactions of the Korea Information Processing Society
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• v.7 no.9
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• pp.3029-3036
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• 2000

Image segmentation is one of the important processes in the image information extraction for computer vision systems. The fuzzy clustering methods have been extensively used in the image segmentation because it extracts feature information of the region. Most of fuzzy clustering methods have used the Fuzzy C-means(FCM) algorithm. This algorithm can be misclassified about the different size of cluster because the degree of membership depends on highly the distance between data and the centroids of the clusters. This paper proposes a fuzzy clustering algorithm using the Average Intracluster Distance that classifies data uniformly without regard to the size of data sets. The Average Intracluster Distance takes an average of the vector set belong to each cluster and increases in exact proportion to its size and density. The experimental results demonstrate that the proposed approach has the g

• The Journal of the Korea Contents Association
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• v.20 no.8
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• pp.637-644
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• 2020

VANET (Vehicular Ad Hoc Networks) is a network between vehicles and between vehicles and infrastructure. VANET-specific characteristics such as high mobility, movement limitation, and signal interference by obstacles make it difficult to provide stable VANET services. To solve this problem, this paper proposes a vehicle type-based priority clustering method that improves the existing bus-based clustering. The proposed algorithm constructs a cluster by evaluating the priority, link quality, and connectivity based on the vehicle type, expected communication lifetime, and link degree of neighbor nodes. It tries to simplify the process of selecting a cluster head and increase cluster coverage by utilizing a predetermined priority based on the type of vehicle. The proposed algorithm is expected to become the basis for activating various services by contributing to providing stable services in a connected car environment.

• Journal of Korea Water Resources Association
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• v.51 no.11
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• pp.959-969
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• 2018

The aging water pipes induce various problems, such as water supply suspension due to breakage, insufficient water pressure, deterioration of water quality, damage by sink holes, and economic losses due to water leaks. However, it is impractical and almost impossible to repair and/or replace all deteriorated water pipes simultaneously. Hence, it is required to quantitatively evaluate the deterioration rate of individual pipes indirect way to determine the rehabilitation order of priority. In this study, ANN(Artificial Neural Network)-Clustering method is suggested as a new approach to assess and assort the water pipes. The proposed method has been applied to a water supply network of YG-county in Jeollanam-do. To assess the applicability of the model, the evaluation results were compared with the results of the Numerical Weighting Method (NWM), which is being currently utilized in practice. The assessment results are depicted in a water pipe map to intuitively grasp the degree of deterioration of the entire pipelines. The application results revealed that the proposed ANN-Clustering models can successfully assess the water pipe deterioration along with the conventional approach of NWM.

• Journal of Korea Multimedia Society
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• v.14 no.10
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• pp.1287-1302
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• 2011

Clustering protocol that is used in wireless sensor network is an efficient method that extends the lifetime of the network. However, when this method is applied to an environment in which collected data of the sensor node easily overlap, sensor nodes unnecessarily consumes energy. In the case of clustering technique that uses a threshold, the lifetime of the network is extended but the degree of accuracy of collected data is low. Therefore it is hard to trust the data and improvement is needed. In addition, it is hard for the clustering protocol that uses multi-hop transmission to normally collect data because the selection of a cluster head node occurs at random and therefore the link of nodes is often disconnected. Accordingly this paper suggested a cluster-formation algorithm that reduces unnecessary energy consumption and that works with an alleviated link disconnection. According to the result of performance analysis, the suggested method lets the nodes consume less energy than the existing clustering method and the transmission efficiency is increased and the entire lifetime is prolonged by about 30%.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.177-187
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• 2002

Spatial data mining is a process to discover interesting relationships and characteristics those exist implicitly in a spatial database. Many spatial clustering algorithms have been developed. But, there are few approaches that focus simultaneously on clustering spatial data and assigning weight to non-spatial attributes of objects. In this paper, we propose a new spatial clustering algorithm, called DBSCAN-W, which is an extension of the existing density-based clustering algorithm DBSCAN. DBSCAN algorithm considers only the location of objects for clustering objects, whereas DBSCAN-W considers not only the location of each object but also its non-spatial attributes relevant to a given application. In DBSCAN-W, each datum has a region represented as a circle of various radius, where the radius means the degree of the importance of the object in the application. We showed that DBSCAN-W is effective in generating clusters reflecting the users requirements through experiments.

• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.717-730
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• 2004

Recently. many researches on the personalization of a web-site have been actively made. The web personalization predicts the sets of the most interesting URLs for each user through data mining approaches such as clustering techniques. Most existing methods using clustering techniques represented the web transactions as bit vectors that represent whether users visit a certain WRL or not to cluster web transactions. The similarity of the web transactions was decided according to the match degree of bit vectors. However, since the existing methods consider only whether users visit a certain URL or not, users' interestingness on the URL is excluded from clustering web transactions. That is, it is possible that the web transactions with different visit proposes or inclinations are classified into the same group. In this paper. we propose an enhanced transaction modeling with interestingness weight to solve such problems and a new similarity measuring method that exploits the proposed transaction modeling. It is shown through performance evaluation that our similarity measuring method improves the accuracy of the web transaction clustering over the existing method.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.189-199
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• 2000

A major deficiency of laboratory testing of rock structure is that the structures are limited in size and therefore present a very small and highly selective sample of the rock mass from which were removed. In a typical engineering project, the samples tested in the laboratory represent only a very small fraction of one percent of the volume of the rock mass. In this paper, we calculate the representative orientation of the resultant vector, the measure of the degree of clustering, the volume of rock mass, the trace length of discontinuity spacing under underlying distributions. And we generate the random fracture networks using real data. We propose the calculating the trace length.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.715-722
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• 2001

Mobile computing has become a reality thank to the convergence of two technologies :powerful portable computers and the wireless networks. The restrictions of wireless network such as bandwidth and energy limitations make data broadcasting an attractive data communication method. This paper addresses the clustering of wireless broadcast data for multipoint queries. By effective clustering of broadcast data the mobile client can access the data on the air in short latency In the paper we define the data affinity and segment affinity measures. The data affinity is the degree that two data objects are accessed by queries, and the segment affinity is the degree that two sets of data (i.e segments) are accessed by queries Our method clusters data objects based on data and segment affinity measures we show that the performance of our method is scarcely infuenced by the growth of the number of queries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1638-1654
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• 2018

The heterogeneous network (HetNet) has been one of the key technologies in Long Term Evolution-Advanced (LTE-A) with growing capacity and coverage demands. However, the introduction of femtocells has brought serious co-layer interference and cross-layer interference, which has been a major factor affecting system throughput. It is generally acknowledged that the resource allocation has significant impact on suppressing interference and improving the system performance. In this paper, we propose a hybrid-clustering algorithm based on the $Mat{\acute{e}}rn$ hard-core process (MHP) to restrain two kinds of co-channel interference in the HetNet. As the impracticality of the hexagonal grid model and the homogeneous Poisson point process model whose points distribute completely randomly to establish the system model. The HetNet model based on the MHP is adopted to satisfy the negative correlation distribution of base stations in this paper. Base on the system model, the spectrum sharing problem with restricted spectrum resources is further analyzed. On the basis of location information and the interference relation of base stations, a hybrid clustering method, which takes into accounts the fairness of two types of base stations is firstly proposed. Then, auction mechanism is discussed to achieve the spectrum sharing inside each cluster, avoiding the spectrum resource waste. Through combining the clustering theory and auction mechanism, the proposed novel algorithm can be applied to restrain the cross-layer interference and co-layer interference of HetNet, which has a high density of base stations. Simulation results show that spectral efficiency and system throughput increase to a certain degree.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.1
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• pp.203-215
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• 2015

The study aims to identify the degree and types of spatial recessions in Si/Gun and Eup/Myun units within Chungcheong region in South Korea to contribute to the efforts being made to diagnose the rural recession and the potentials. To this end, we analyzed 27 Sis and Guns to identify the degree of recession and potentials of rural areas in Chungcheong region. We also carried out the diagnosis and K-Means Clustering on 274 Eups and Myuns, smaller administrative units, to figure out the types and characteristics of the rural recessions. In case of the analysis targeting the Sis and Guns, a relatively high degree of rural recession was found in Cheongyang, Seocheon and Taean for Chungcheongnam-do, and in Danyang and Goisan, as well as in Boeun, Okcheon and Youngdong - which are collectively called as 'Southern 3 Areas in Chungcheongbuk-do' as they are conventionally known by their high degree of rural recession. According to the results of the clustering analysis carried out on the 166 Eups and Myuns, there were five outstanding clusters. They were; areas with housing deterioration (29), areas with poor economic foundation (16), areas with poor accessibility to central areas (42), areas with poor residential environment (51) and areas with aged population (28). The findings and results of the present study are likely to serve as a basis for the design and enforcement of forthcoming rural area activation policies. Also, it would be highly recommended that a more comprehensive diagnosis is taken from a community-level perspective and policy suggestions and strategies tailored for rural communities are further discussed.