• ETRI Journal
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• v.38 no.2
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• pp.314-325
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• 2016

This paper examines the resource gain that can be obtained from the creation of clusters of nodes in densely populated areas. A single node within each such cluster is designated as a "hotspot"; all other nodes then communicate with a destination node, such as a base station, through such hotspots. We propose a semi-distributed algorithm, referred to as coordinated cognitive tethering (CCT), which clusters all nodes and coordinates hotspots to tether over locally available white spaces. CCT performs the following these steps: (a) groups nodes based on a modified k-means clustering algorithm; (b) assigns white-space spectrum to each cluster based on a distributed graph-coloring approach to maximize spectrum reuse, and (c) allocates physical-layer resources to individual users based on local channel information. Unlike small cells (for example, femtocells and WiFi), this approach does not require any additions to existing infrastructure. In addition to providing parallel service to more users than conventional direct communication in cellular networks, simulation results show that CCT can increase the average battery life of devices by 30%, on average.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3203-3215
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• 2015

Due to the high growth of SNS population, service scalability is one of the critical issues to be addressed. The cloud environment provides the flexible computing and storage resources for services deployment, which fits the characteristics of scalable SNS deployment. However, if the SNS related information is not properly placed, it will cause unbalance load and heavy transmission cost on the storage virtual machine (VM) and cloud data center (CDC) network. In this paper, we characterize the SNS into a graph model based on the users' associations and interest correlations. The node weight represents the degree of associations, which can be indexed by the number of friends or data sources, and the link weight denotes the correlation between users/data sources. Then, based on the SNS graph, the two-step algorithm is proposed in this paper to determine the placement of SNS related data among VMs. Two k-means based clustering schemes are proposed to allocate social data in proper VM and physical servers for pre-configured VM and dynamic VM environment, respectively. The experimental example was conducted and to illustrate and compare the performance of the proposed schemes.

• The Journal of the Korea Contents Association
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• v.18 no.7
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• pp.449-458
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• 2018

As the usage of social network services increases, event information occurring offline is spreading more rapidly. Therefore, studies have been conducted to detect events by analyzing social data. In this paper, we propose a graph based event detection scheme considering user interest in social networks. The proposed scheme constructs a keyword graph by analyzing tweets posted by users. We calculates the interest measure from users' social activities and uses it to identify events by considering changes in interest. Therefore, it is possible to eliminate events that are repeatedly posted without meaning and improve the reliability of the results. We conduct various performance evaluations to demonstrate the superiority of the proposed event detection scheme.

• Korean Journal of Computational Design and Engineering
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• v.6 no.2
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• pp.78-88
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• 2001

In order to adopt feature-based parametric modeling, CAD/CAM applications must have a geometric constraint solver that can handle a large set of geometric configurations efficiently and robustly. In this paper, we describe a graph constructive approach to solving geometric constraint problems. Usually, a graph constructive approach is efficient, however it has its limitation in scope; it cannot handle ruler-and-compass non-constructible configurations and under-constrained problems. To overcome these limitations. we propose an algorithm that isolates ruler-and-compass non-constructible configurations from ruler-and-compass constructible configurations and applies numerical calculation methods to solve them separately. This separation can maximize the efficiency and robustness of a geometric constraint solver. Moreover, the solver can handle under-constrained problems by classifying under-constrained subgraphs to simplified cases by applying classification rules. Then, it decides the calculating sequence of geometric entities in each classified case and calculates geometric entities by adding appropriate assumptions or constraints. By extending the clustering types and defining several rules, the proposed approach can overcome limitations of previous graph constructive approaches which makes it possible to develop an efficient and robust geometric constraint solver.

• The Korean Journal of Applied Statistics
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• v.33 no.2
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• pp.115-122
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• 2020

K-means clustering uses a spherical or elliptical metric to group data points; however, it does not work well for non-convex data such as the concentric circles. Spectral clustering, based on graph theory, is a generalized and robust technique to deal with non-standard type of data such as non-convex data. Results obtained by spectral clustering often outperform traditional clustering such as K-means. In this paper, we review spectral clustering and show important issues in spectral clustering such as determining the number of clusters K, estimation of scale parameter in the adjacency of two points, and the dimension reduction technique in clustering high-dimensional data.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.6
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• pp.1-8
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• 2009

Detecting entry and exit zones in a view covered by multiple cameras is an essential step to determine the topology of the camera setup, which is critical for achieving and sustaining the accuracy and efficiency of multi-camera surveillance system. In this paper, a graph theoretic clustering method is proposed to detect zones using data points which correspond to entry and exit events of objects in the camera view. The minimum spanning tree (MST) is constructed by associating the data points. Then a set of well-formed clusters is sought by removing inconsistent edges of the MST, based on the concepts of the cluster balance and the cluster density defined in the paper. Experimental results suggest that the proposed method is effective, even for sparsely elongated clusters which could be problematic for expectation-maximization (EM). In addition, comparing to the EM-based approaches, the number of data required to obtain stable outcome is relatively small, hence shorter learning period.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1254-1271
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• 2018

Keyphrase extraction is one of fundamental natural language processing (NLP) tools to improve many text-mining applications such as document summarization and clustering. In this paper, we propose to use two novel techniques on the top of the state-of-the-art keyphrase extraction methods. First is the anti-patterns that aim to recognize non-keyphrase candidates. The state-of-the-art methods often used the rich feature set to identify keyphrases while those rich feature set cover only some of all keyphrases because keyphrases share very few similar patterns and stylistic features while non-keyphrase candidates often share many similar patterns and stylistic features. Second one is to use the dependency graph instead of the word co-occurrence graph that could not connect two words that are syntactically related and placed far from each other in a sentence while the dependency graph can do so. In experiments, we have compared the performances with different settings of the graphs (co-occurrence and dependency), and with the existing method results. Finally, we discovered that the combination method of dependency graph and anti-patterns outperform the state-of-the-art performances.

• The Journal of Society for e-Business Studies
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• v.8 no.2
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• pp.77-98
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• 2003

A web-based information system, that is a dominant type of information systems, suffers from the "web crisis" in development and maintenance of the system. To cope with the problem, a technology of software clustering to web-based application, which is one of web engineering, is strongly needed. In this paper, we propose a Form Clustering Algorithm along with an application example, which are used for internal-system reengineering to web-based information system. A Form Clustering Algorithm focuses on Page-model which is the feature of the web among the various web-based information system's structural model. Specially, we applying distance matrix to navigation model of graph form for easily analyzing, and web log analysis for identifying core function object that have a highly loading. Also, we create web software structure that can be used to maximize reusability and assign hardware effectively through 2-phase clustering step. Form Clustering Algorithm might be used at web-based information system development and maintenance for reusable web component development and hardware assignment, respectively.

• Journal of KIISE
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• v.45 no.1
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• pp.15-21
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• 2018

Low-Rank Representation (LRR) based methods are widely used in many practical applications, such as face clustering and object detection, because they can guarantee high prediction accuracy when used to constructing graphs in graph - based semi-supervised learning. However, in order to solve the LRR problem, it is necessary to perform singular value decomposition on the square matrix of the number of data points for each iteration of the algorithm; hence the calculation is inefficient. To solve this problem, we propose an improved and faster LRR method based on the recently published Fast LRR (FaLRR) and suggests ways to introduce and optimize additional constraints on the underlying optimization goals in order to address the fact that the FaLRR is fast but actually poor in classification problems. Our experiments confirm that the proposed method finds a better solution than LRR does. We also propose Fast MLRR (FaMLRR), which shows better results when the goal of minimizing is added.

• The Transactions of the Korea Information Processing Society
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• v.5 no.8
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• pp.2041-2049
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• 1998

This paper suggests a technique for program segmentation and maintenance using PSDG(Post-State Dependency Graph) that improves the quality of a software by identifying and detecting defects in already fixed source code. A program segmentation is performed by utilizing source code analysis which combines the measures of static, dynamic and semantic slicing when we need understandability of defect in programs for corrective maintanence. It provides users with a segmental principle to split a program by tracing state dependency of a source code with the graph, and clustering and highlighting, Through a modeling of the PSDG, elimination of ineffective program deadcode and generalization of related program segments arc possible, Additionally, it can be correlated with other design modeb as STD(State Transition Diagram), also be used as design documents.