• Wind and Structures
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• v.39 no.1
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• pp.1-14
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• 2024

Widespread damages from extreme winds have attracted lots of attentions of the resilience assessment of power distribution systems. With many related environmental parameters as well as numerous power infrastructure components, such as poles and wires, the increased challenge of power asset management before, during and after extreme events have to be addressed to prevent possible cascading failures in the power distribution system. Many extreme winds from weather events, such as hurricanes, generate widespread damages in multiple areas such as the economy, social security, and infrastructure management. The livelihoods of residents in the impaired areas are devastated largely due to the paucity of vital utilities, such as electricity. To address the challenge of power grid asset management, power system clustering is needed to partition a complex power system into several stable clusters to prevent the cascading failure from happening. Traditionally, system clustering uses the Binary Decision Diagram (BDD) to derive the clustering result, which is time-consuming and inefficient. Meanwhile, the previous studies considering the weather hazards did not include any detailed weather-related meteorologic parameters which is not appropriate as the heterogeneity of the parameters could largely affect the system performance. Therefore, a fragility-based network hierarchical spectral clustering method is proposed. In the present paper, the fragility curve and surfaces for a power distribution subsystem are obtained first. The fragility of the subsystem under typical failure mechanisms is calculated as a function of wind speed and pole characteristic dimension (diameter or span length). Secondly, the proposed fragility-based hierarchical spectral clustering method (F-HSC) integrates the physics-based fragility analysis into Hierarchical Spectral Clustering (HSC) technique from graph theory to achieve the clustering result for the power distribution system under extreme weather events. From the results of vulnerability analysis, it could be seen that the system performance after clustering is better than before clustering. With the F-HSC method, the impact of the extreme weather events could be considered with topology to cluster different power distribution systems to prevent the system from experiencing power blackouts.

• Journal of Information Management
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• v.40 no.2
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• pp.95-115
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• 2009

Author resolution is to disambiguate same-name author occurrences into real individuals. For this, pair-wise author similarities are computed for author name entities, and then clustering is performed. So far, many studies have employed hierarchical clustering techniques for author disambiguation. However, various hierarchical clustering methods have not been sufficiently investigated. This study covers an empirical evaluation and analysis of hierarchical clustering applied to Korean author resolution, using multiple distance functions such as Dice coefficient, Cosine similarity, Euclidean distance, Jaccard coefficient, Pearson correlation coefficient.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.673-676
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• 2004

Fast and high-quality document clustering algorithms play an important role in providing data exploration by organizing large amounts of information into a small number of meaningful clusters. In particular, hierarchical clustering provide a view of the data at different levels, making the large document collections are adapted to people's instinctive and interested requires. Many papers have shown that the hierarchical clustering method takes good-performance, but is limited because of its quadratic time complexity. In contrast, K-means has a time complexity that is linear in the number of documents, but is thought to produce inferior clusters. Think of the factor of simpleness, high-quality and high-efficiency, we combine the two approaches providing a new system named CONDOR system [10] with hierarchical structure based on document clustering using K-means algorithm to "get the best of both worlds". The performance of CONDOR system is compared with the VIVISIMO hierarchical clustering system [9], and performance is analyzed on feature words selection of specific topics and the optimum hierarchy depth.

• Journal of Haehwa Medicine
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• v.23 no.1
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• pp.115-124
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• 2014

Objectives : There are plenty of medical record of acupuncture & moxibustion in Traditional East Asian medicine(TEAM). We performed this study to find out the hidden criteria lies on this record to choose proper acupoints. Methods : "Zhenjiuzishengjing", ancient TEAM book was analysed using document clustering techniques. Corpus was made from this book. It contained 196 texts driven from each symptoms. Each texts converted to vector representing frequency of 349 acupoints. Distance of vectors calculated by weighted Euclidean distance method. According to this distances, hierarchical clustering of symptoms was builded. Results : The cluster consisted of five large groups. they had high corelation with body part; head and face, chest, abdomen, upper extremity, lower extremity, back. Conclusions : It assumes that body part of symptom is the most importance criteria of acupoints selecting. some high similar symptom vectors consolidated this result. the other criteria is cause and pathway of illness. some symptoms bound together which had common cause and pathway.

• Journal of Internet of Things and Convergence
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• v.3 no.1
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• pp.9-11
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• 2017

Recently, with the widespread use of the Internet and mobile devices, the number of attacks by hackers using the network is increasing. When connecting a network, packets are exchanged and communicated, which includes various information. We analyze the information of these packets using hierarchical clustering analysis and classify normal and abnormal packets to detect attacks. With this analysis method, it will be possible to detect attacks by analyzing new packets.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.187-190
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• 2001

Due to the recent proliferation of the internet, it is broadly granted that the necessity of the automatic document categorization has been on the rise. Since it is a heavy time-consuming work and takes too much manpower to process and classify manually, we need a system that categorizes them automatically as their contents. In this paper, we propose the automatic E-mail response system that is based on 2 hierarchical document clustering methods. One is to get the final result from the classifier trained seperatly within each class, after clustering the whole documents into 3 groups so that the first classifier categorize the input documents as the corresponding group. The other method is that the system classifies the most distinct classes first as their similarity, successively. Neural networks have been adopted as classifiers, we have used dendrograms to show the hierarchical aspect of similarities between classes. The comparison among the performances of hierarchical and non-hierarchical classifiers tells us clustering methods have provided the classification efficiency.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.1
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• pp.61-69
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• 2016

Understanding load patterns and customer classification is a basic step in analyzing the behavior of electricity consumers. To achieve that, there have been many researches about clustering customers' daily load data. Nowadays, the deployment of advanced metering infrastructure (AMI) and big-data technologies make it easier to study customers' load data. In this paper, we study load clustering from the view point of yearly and daily load pattern. We compare four clustering methods; K-means clustering, hierarchical clustering (average & Ward's method) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). We also discuss the relationship between clustering results and Korean Standard Industrial Classification that is one of possible labels for customers' load data. We find that hierarchical clustering with Ward's method is suitable for clustering load data and KSIC can be well characterized by daily load pattern, but not quite well by yearly load pattern.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1353-1359
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• 2010

Basic objective in cluster analysis is to discover natural groupings of items. In general, clustering is conducted based on some similarity (or dissimilarity) matrix or the original input data. Various measures of similarities between objects are developed. In this paper, we consider a clustering of huge categorical real data set which shows the aspects of time-location-activity of Korean people. Some useful similarity measure for the data set, are developed and adopted for the categorical variables. Hierarchical and nonhierarchical clustering method are applied for the considered data set which is huge and consists of many categorical variables.

• Journal of Korean Neurosurgical Society
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• v.52 no.5
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• pp.466-471
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• 2012

Objective : The aim of this study was to devise an objective clustering method for magnetoencephalography (MEG) interictal spike sources, and to identify the prognostic value of the new clustering method in adult epilepsy patients with cortical dysplasia (CD). Methods : We retrospectively analyzed 25 adult patients with histologically proven CD, who underwent MEG examination and surgical resection for intractable epilepsy. The mean postoperative follow-up period was 3.1 years. A hierarchical clustering method was adopted for MEG interictal spike source clustering. Clustered sources were then tested for their prognostic value toward surgical outcome. Results : Postoperative seizure outcome was Engel class I in 6 (24%), class II in 3 (12%), class III in 12 (48%), and class IV in 4 (16%) patients. With respect to MEG spike clustering, 12 of 25 (48%) patients showed 1 cluster, 2 (8%) showed 2 or more clusters within the same lobe, 10 (40%) showed 2 or more clusters in a different lobe, and 1 (4%) patient had only scattered spikes with no clustering. Patients who showed focal clustering achieved better surgical outcome than distributed cases (p=0.017). Conclusion : This is the first study that introduces an objective method to classify the distribution of MEG interictal spike sources. By using a hierarchical clustering method, we found that the presence of focal clustered spikes predicts a better postoperative outcome in epilepsy patients with CD.

• Korean Management Science Review
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• v.19 no.1
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• pp.189-196
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• 2002

Data clustering is often one of the first steps in data mining analysis. It Identifies groups of related objects that can be used as a starling point for exploring further relationships. This technique supports the development of population segmentation models, such as demographic-based customer segmentation. This paper Purpose to present the development of two phase hierarchical clustering algorithm for group formation. Applications of the algorithm for product-customer group formation in customer relationahip management are also discussed. As a result of computer simulations, suggested algorithm outperforms single link method and k-means clustering.