• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.587-592
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• 2003

Many real world control problems have continuous states and actions. When the state space is continuous, the reinforcement learning problems involve very large state space and suffer from memory and time for learning all individual state-action values. These problems need function approximators that reason action about new state from previously experienced states. We introduce Fuzzy Q-Map that is a function approximators for 1 - step Q-learning and is based on fuzzy clustering. Fuzzy Q-Map groups similar states and chooses an action and refers Q value according to membership degree. The centroid and Q value of winner cluster is updated using membership degree and TD(Temporal Difference) error. We applied Fuzzy Q-Map to the mountain car problem and acquired accelerated learning speed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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• pp.1256-1275
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• 2014

As one of the new self-organizing and self-configuration broadband networks, wireless mesh networks are being increasingly attractive. In order to solve the load balancing problem in wireless mesh networks, this paper proposes a novel multi-path routing algorithm based on clustering (Cluster_MMesh) for wireless mesh networks. In the clustering stage, on the basis of the maximum connectivity clustering algorithm and k-hop clustering algorithm, according to the idea of maximum connectivity, a new concept of node connectivity degree is proposed in this paper, which can make the selection of cluster head more simple and reasonable. While clustering, the node which has less expected load in the candidate border gateway node set will be selected as the border gateway node. In the multi-path routing establishment stage, we use the intra-clustering multi-path routing algorithm and inter-clustering multi-path routing algorithm to establish multi-path routing from the source node to the destination node. At last, in the traffic allocation stage, we will use the virtual disjoint multi-path model (Vdmp) to allocate the network traffic. Simulation results show that the Cluster_MMesh routing algorithm can help increase the packet delivery rate, reduce the average end to end delay, and improve the network performance.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.141-148
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• 2016

In this paper, we propose a topic network analysis approach which integrates topic modeling and social network analysis. We collected 2,039 scientific papers from five top journals in the field of data mining published from 1996 to 2015, and analyzed them with the proposed approach. To identify topic trends, time-series analysis of topic network is performed based on 4 intervals. Our experimental results show centralization of the topic network has the highest score from 1996 to 2000, and decreases for next 5 years and increases again. For last 5 years, centralization of the degree centrality increases, while centralization of the betweenness centrality and closeness centrality decreases again. Also, clustering is identified as the most interrelated topic among other topics. Topics with the highest degree centrality evolves clustering, web applications, clustering and dimensionality reduction according to time. Our approach extracts the interrelationships of topics, which cannot be detected with conventional topic modeling approaches, and provides topical trends of data mining research fields.

• Journal of the Korea Society of Computer and Information
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• v.26 no.2
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• pp.39-44
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• 2021

In this paper, we propose a method of disease diagnosis system that can diagnose the health status of household pets for the people who lack veterinary knowledge. The proposed diagnosis system holds 50 different kinds of diseases with the symptoms for each of them as a database to provide results from symptom input. Each disease database has its own symptom codes for a disease, and by using the disease database, FCM clustering technique is applied to disease which outputs membership degree to determine diseases close to the input symptom as a pet diagnosis result. The implementation results of the proposed pet diagnosis system were obtained by the number of selected symptoms and the possibility values of the diseases that have the selected symptoms being sorted in descending order to derive top 3 diseases closest to the pet's symptom.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.468-471
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• 2015

The study aims to identify the degree and types of spatial decline in Eup/Myun units within Chungcheong region in South Korea to contribute to the efforts being made to diagnose the rural decline and the potentials. To this end, we analyzed 27 Sis and Guns to identify the degree of decline and potentials of rural areas in Chungcheong region. We also carried out the diagnosis and K-Means Clustering on 274 Eups and Myuns, the smallest administrative units, to figure out the types and characteristics of the rural recessions. 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.

• Journal of Korea Port Economic Association
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• v.28 no.1
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• pp.53-82
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• 2012

The purpose of this paper is to show the clustering trend by using the context-dependent and measure-specific models for 38 Asian ports during 10 years(2001-2009) with 4 inputs and 1 output. The main empirical results of this paper are as follows. First, clustering results by using context-dependent and measure-specific models are same. Second, the most efficient clustering was shown among the Hong Kong, Singapore, Ningbo, Guangzhou, and Kaosiung ports. Third, Port Sultan Qaboos, Jeddah, and Aden ports showed the lowest level clustering. Fourth, ranking order of attractiveness is Guangzhou, Dubai, HongKong, Ningbo, and Shanghai, and the results of progressive scores confirmed that low level ports can increase their efficiency by benchmarking the upper level ports. Fifth, benchmark share showed that Dubai(birth length), and HongKong(port depth, total area, and no. of cranes) have affected the efficiency of the inefficient ports.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1836-1842
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• 2015

On road networks, the clustering of moving objects is important for traffic monitoring and routes recommendation. The existing schemes find out density route by considering the number of vehicles in a road segment. Since they don’t consider the features of each road segment such as width, length, and directions in a road network, the results are not correct in some real road networks. To overcome such problems, we propose a clustering method for congested routes discovering from the trajectories of moving objects on road networks. The proposed scheme can be divided into three steps. First, it divides each road network into segments with different width, length, and directions. Second, the congested road segments are detected through analyzing the trajectories of moving objects on the road network. The saturation degree of each road segment and the average moving speed of vehicles in a road segment are computed to detect the congested road segments. Finally, we compute the final congested routes by using a clustering scheme. The experimental results showed that the proposed scheme can efficiently discover the congested routes in different directions of the roads.

• Journal of Electrical Engineering and Technology
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• v.2 no.2
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• pp.157-164
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• 2007

Diagnosis techniques based on the dissolved gas analysis(DGA) have been developed to detect incipient faults in power transformers. Various methods exist based on DGA such as IEC, Roger, Dornenburg, and etc. However, these methods have been applied to different problems with different standards. Furthermore, it is difficult to achieve an accurate diagnosis by DGA without experienced experts. In order to resolve these drawbacks, this paper proposes a novel diagnosis method using fuzzy clustering and a radial basis neural network(RBFNN). In the neural network, fuzzy clustering is effective for selecting the efficient training data and reducing learning process time. After fuzzy clustering, the RBF neural network is developed to analyze and diagnose the state of the transformer. The proposed method measures the possibility and degree of aging as well as the faults occurred in the transformer. To demonstrate the validity of the proposed method, various experiments are performed and their results are presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3121-3143
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• 2019

Segmentation plays an important role in the field of image processing and computer vision. Intuitionistic fuzzy C-means (IFCM) clustering algorithm emerged as an effective technique for image segmentation in recent years. However, standard fuzzy C-means (FCM) and IFCM algorithms are sensitive to noise and initial cluster centers, and they ignore the spatial relationship of pixels. In view of these shortcomings, an improved algorithm based on IFCM is proposed in this paper. Firstly, we propose a modified non-membership function to generate intuitionistic fuzzy set and a method of determining initial clustering centers based on grayscale features, they highlight the effect of uncertainty in intuitionistic fuzzy set and improve the robustness to noise. Secondly, an improved nonlinear kernel function is proposed to map data into kernel space to measure the distance between data and the cluster centers more accurately. Thirdly, the local spatial-gray information measure is introduced, which considers membership degree, gray features and spatial position information at the same time. Finally, we propose a new measure of intuitionistic fuzzy entropy, it takes into account fuzziness and intuition of intuitionistic fuzzy set. The experimental results show that compared with other IFCM based algorithms, the proposed algorithm has better segmentation and clustering performance.

• Journal of Korea Multimedia Society
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• v.25 no.6
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• pp.886-893
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• 2022

Outlier detection refers to the task of detecting data that deviate significantly from the normal data distribution. Most outlier detection methods compute an outlier score which indicates the degree to which a data sample deviates from normal. However, setting a threshold for an outlier score to determine if a data sample is outlier or normal is not trivial. In this paper, we propose a binary prediction method for outlier detection based on spectral clustering and one-class SVM ensemble. Given training data consisting of normal data samples, a clustering method is performed to find clusters in the training data, and the ensemble of one-class SVM models trained on each cluster finds the boundaries of the normal data. We show how to obtain a threshold for transforming outlier scores computed from the ensemble of one-class SVM models into binary predictive values. Experimental results with high dimensional text data show that the proposed method can be effectively applied to high dimensional data, especially when the normal training data consists of different shapes and densities of clusters.