• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.763-770
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• 2010

This paper proposes an algorithm to enhance the performance of the spatial partitioning method for a flocking behavior. One of the characteristics in a flocking behavior is that two agents may share many common neighbors if they are spatially close to each other. This paper improves the spatial partitioning method by applying this characteristic. While the conventional spatial partitioning method computes the k-nearest neighbors of an agent one by one, the proposed method computes simultaneously the k-nearest neighbors of agents if they are spatially close to each other. The proposed algorithm was implemented and its performance was experimentally compared with the original spatial partitioning method. The results of the comparison showed that the proposed algorithm outperformed the original method by about 33% in average.

• Journal of Intelligence and Information Systems
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• v.13 no.2
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• pp.1-14
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• 2007

Case-based reasoning (CBR) is one of the most widely applied data mining techniques and has proven its effectiveness in various domains. Since CBR is basically based on k-Nearest Neighbors (NN) method, the value of k affects the performance of CBR model directly. Once the value of k is set, it is fixed for the lifetime of the CBR model. However, if the value is set greater or smaller than the optimal value, the performance of CBR model will be deteriorated. In this research, we propose a new method of composing the NN set using similarity scores as themselves, which we shall call s-NN method, rather than using the fixed value of k. In the s-NN method, the different number of nearest neighbors can be selected for each new case. Performance evaluation using the data from UCI Machine Learning Repository shows that the CBR model adopting the s-NN method outperforms the CBR model adopting the traditional k-NN method.

• Journal of Korea Game Society
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• v.9 no.2
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• pp.115-123
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• 2009

This paper proposes an algorithm to improve the performance of the spatial partitioning method for flocking behaviors. The core concept is to improve the performance by using the fact that even if a moving entity, boid in flock continuously changes its direction and position, its k-nearest neighbors, kNN to effect on decision of the next direction is not changed frequently. From the previous kNN, the method to check whether new kNN is changed or not is proposed in this paper and then the correctness of the proposed method is proved by two theorems. The proposed algorithm was implemented and its performance was compared with the conventional spatial partitioning method. The results of the comparison show that the proposed algorithm outperforms the conventional one by about 30% with respect to the number of frames per a second.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1059-1068
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• 2021

In urban areas, GPS(Global Positioning System)/GNSS(Global Navigation Satellite System) signals are blocked or distorted by structures such as buildings, which limits positioning. To compensate for this problem, in this paper, fingerprinting-based positioning using RSRP(: Reference Signal Received Power) information of LTE signals is performed. The W-KNN(Weighted - K Nearest Neighbors) technique, which is widely used in the positioning step of fingerprinting, yields different positioning performance results depending on the similarity distance calculation method and weighting method used in correlation. In this paper, the performance of the fingerprinting positioning according to the techniques used in correlation is comparatively analyzed experimentally.

• Journal of Computing Science and Engineering
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• v.11 no.1
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• pp.1-8
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• 2017

Naive Bayes nearest neighbor (NBNN) is a simple image classifier based on identifying nearest neighbors. NBNN uses original image descriptors (e.g., SIFTs) without vector quantization for preserving the discriminative power of descriptors and has a powerful generalization characteristic. However, it has a distinct disadvantage. Its memory requirement can be prohibitively high while processing a large amount of data. To deal with this problem, we apply a spherical hashing binary code embedding technique, to compactly encode data without significantly losing classification accuracy. We also propose using an inverted index to identify nearest neighbors among binarized image descriptors. To demonstrate the benefits of our method, we apply our method to two existing NBNN techniques with an image dataset. By using 64 bit length, we are able to reduce memory 16 times with higher runtime performance and no significant loss of classification accuracy. This result is achieved by our compact encoding scheme for image descriptors without losing much information from original image descriptors.

• Journal of Navigation and Port Research
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• v.33 no.2
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• pp.143-151
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• 2009

The field of vehicle routing is currently growing rapidly because of many actual applications in truckload and less than truckload trucking, courier services, door to door services, and many other problems that generally hinder the optimization of transportation costs in a logistics network. The rapidly increasing number of customers in such a network has caused problems such as difficulty in cost optimization in terms of getting a global optimum solution in an acceptable time. Fast algorithms are needed to find sufficient solutions in a limited time that can be used for real time scheduling. In this paper, the nearest L-method (NLNM) is proposed to obtain a vehicle routing solution. String neighbors of different lengths were chosen, tested and compared. The applied de crossing procedure is meant to solve the routes by NLNM by giving a better solution and shorter computation time than that of NLNM with long string neighbors.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.401-410
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• 2021

Popular in discriminant classification analysis, k-nearest neighbor classification methods have limitations that do not reflect the local characteristic of the data, considering only the number of fixed neighbors. Considering the local structure of the data, the adaptive nearest neighbor method has been developed to select the number of neighbors. In the analysis of high-dimensional data, it is common to perform dimension reduction such as random projection techniques before using k-nearest neighbor classification. Recently, an ensemble technique has been developed that carefully combines the results of such random classifiers and makes final assignments by voting. In this paper, we propose a novel discriminant classification technique that combines adaptive nearest neighbor methods with random projection ensemble techniques for analysis on high-dimensional data. Through simulation and real-world data analyses, we confirm that the proposed method outperforms in terms of classification accuracy compared to the previously developed methods.

• ETRI Journal
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• v.40 no.4
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• pp.531-536
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• 2018

The weighted K-nearest neighbor (WKNN) algorithm is used to reduce positioning accuracy, as it uses a fixed number of neighbors to estimate the position. In this paper, we propose a dynamic threshold location algorithm (DH-KNN) to improve positioning accuracy. The proposed algorithm is designed based on a dynamic threshold to determine the number of neighbors and filter out singular reference points (RPs). We compare its performance with the WKNN and Enhanced K-Nearest Neighbor (EKNN) algorithms in test spaces of networks with dimensions of $20m{\times}20m$, $30m{\times}30m$, $40m{\times}40m$ and $50m{\times}50m$. Simulation results show that the maximum position accuracy of DH-KNN improves by 31.1%, and its maximum position error decreases by 23.5%. The results demonstrate that our proposed method achieves better performance than other well-known algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3714-3732
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• 2019

With the rapid development of network, Intrusion Detection System(IDS) plays a more and more important role in network applications. Many data mining algorithms are used to build IDS. However, due to the advent of big data era, massive data are generated. When dealing with large-scale data sets, most data mining algorithms suffer from a high computational burden which makes IDS much less efficient. To build an efficient IDS over big data, we propose a classification algorithm based on data clustering and data reduction. In the training stage, the training data are divided into clusters with similar size by Mini Batch K-Means algorithm, meanwhile, the center of each cluster is used as its index. Then, we select representative instances for each cluster to perform the task of data reduction and use the clusters that consist of representative instances to build a K-Nearest Neighbor(KNN) detection model. In the detection stage, we sort clusters according to the distances between the test sample and cluster indexes, and obtain k nearest clusters where we find k nearest neighbors. Experimental results show that searching neighbors by cluster indexes reduces the computational complexity significantly, and classification with reduced data of representative instances not only improves the efficiency, but also maintains high accuracy.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.6
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• pp.1223-1229
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• 2009

The performance of Radial Basis Function Neural Networks depends on setting up the Radial Basis Functions over the input space which are the important design procedure of Radial Basis Function Neural Networks. The existing method to initialize the location of the radial basis functions over the input space is to use the conditional fuzzy C-means clustering. However, the researchers which are interested in the conditional fuzzy C-means clustering cannot get as good modeling performance as they expect because the conditional fuzzy C-means clustering cannot project the information which is extracted over the output space into the input space. To compensate the above mentioned drawback of the conditional fuzzy C-means clustering, we apply a fuzzy K-nearest neighbors approach to project the auxiliary information defined over the output space into the input space without lose of the information.