• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.469-470
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• 2015

우리는 그룹핑 k-최근접 (Grouping k Nearest Neighbor; GkNN)질의를 지원하기 위하여 유연한 최근접객체(Flexible Nearest Neighbor; FNN)검색 방법을 제안한다. GkNN이란 기존에 제안된 kNN과 다르게 질의자가 요청한 k개의 객체를 모두 확인한 후에 이동 경로의 총합이 가장 작은 k개의 객체를 검색하는 방법이다. 기존 연구에서 제안된 최근접 객체들 (Nearest Neighborhood; NNH) 또한 이 문제를 해결하기 위하여 제안되었다. 그러나 NNH의 문제점은 객체 k와 p가 고정되어 있기 때문에 이동 환경에서 q에서 C까지의 거리가 증가하는 것이다. FNN의 환경은 NNH의 환경과 유사하다. 우리는 NNH의 q에서 집합 C 중 거리 중 가장 짧은 $c_i$ 선택한 후 q에서 $c_i$에 포함된 객체들 모두 검색하는 이동 경로의 총합과 FNN의 이동경로의 총 합을 비교하여 NNH의 문제점을 해결하였다.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.2 s.332
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• pp.83-88
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• 2005

We propose the design method for the VLSI architecture of FNNPDS combined PDS(partial distance search) and FNNS(fast nearest neighbor search), which are used to fast encoding in vector quantization, and obtain the results that FNNPDS(fast nearest neighbor partial distance search) is faster method than the conventional methods by simulation. In simulations, we investigate timing diagrams described searching time of the nearest codevector for an input vector, and compare the average clock cycles per input vector for Lena and Peppers images. According to the result of simulations, the number of the clock cycle of FNNPDS was reduced to $79.2\%\~11.7\%$ as compared with the number using the conventional techniques.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.677-680
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• 2004

In the mobile environment, according to the movement of the object, the query finds the nearest special object or place from object position. However, because query object moves continuously in the mobile environment, query demand changes according to the direction attribute of query object. Also, in the case of moving of query object and simply the minimum distance value of query result, sometimes we find the result against the query object direction. Especially, in most road condition, as user has to return after reaching U-turn area, user rather spends time and cost. Therefore, in order to solve those problems, in this paper we propose the nearest neighbor method considering moving object position and direction for mobile recommendation system.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.871-875
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• 2008

This paper deals with the feature selection for multiple k-nearest neighbor (k-NN) classifiers using Genetic Algorithm with Varying reputation Size (GAVaPS). Because we use multiple k-NN classifiers, the feature selection problem for them is vary hard and has large search region. To solve this problem, we employ the GAVaPS which outperforms comparison with simple genetic algorithm (SGA). Further, we propose the efficient combining method for multiple k-NN classifiers using GAVaPS. Experiments are performed to demonstrate the efficiency of the proposed method.

• Journal of Korea Spatial Information System Society
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• v.9 no.2
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• pp.67-79
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• 2007

Recently, to support LBS(location-based services) and telematics applications efficiently, there have been many researches which consider the spatial network instead of Euclidean space. However, existing range query and k-nearest neighbor query algorithms show a linear decrease in performance as the value of radius and k is increased. In this paper, to increase the performance of query processing algorithm, we propose materialization-based range and k-nearest neighbor algorithms. In addition, we make the performance comparison to show the proposed algorithm achieves better retrieval performance than the existing algorithm.

• Journal of KIISE:Software and Applications
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• v.36 no.4
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• pp.273-282
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• 2009

Missing data is one of the common problems in building analysis or prediction models using software project data. Missing imputation methods are known to be more effective missing data handling method than deleting methods in small software project data. While K nearest neighbor imputation is a proper missing imputation method in the software project data, it cannot use non-missing information of incomplete project instances. In this paper, we propose an approach to missing data imputation for numerical software project data by combining K nearest neighbor and maximum likelihood estimation; we also extend the average absolute error measure by normalization for accurate evaluation. Our approach overcomes the limitation of K nearest neighbor imputation and outperforms on our real data sets.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.7
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• pp.377-383
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• 2002

The TSP(Traveling Salesman Problem) has been known as NP-complete, there have been various studies to find the near optimal solution. The nearest neighbor heuristic is more simple than the other algorithms which are to find the optimal solution. This paper designs and implements a new distributed nearest neighbor heuristic with bounding function for the TSP using the master/slave model of PVM(Parallel Virtual Machine). Distributed genetic algorithm obtains a near optimal solution and distributed nearest neighbor heuristic finds an optimal solution for the TSP using the near optimal value obtained by distributed genetic algorithm as the initial bounding value. Especially, we get more speedup using a new genetic operator in the genetic algorithm.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.53-60
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• 2012

Nearest-neighbor classification predicts the class of an input data with the most frequent class among the near training data of the input data. Even though nearest-neighbor classification doesn't have a training stage, all of the training data are necessary in a predictive stage and the generalization performance depends on the quality of training data. Therefore, as the training data size increase, a nearest-neighbor classification requires the large amount of memory and the large computation time in prediction. In this paper, we propose a prototype selection algorithm that predicts the class of test data with the new set of prototypes which are near-boundary training data. Based on Tomek links and distance metric, the proposed algorithm selects boundary data and decides whether the selected data is added to the set of prototypes by considering classes and distance relationships. In the experiments, the number of prototypes is much smaller than the size of original training data and we takes advantages of storage reduction and fast prediction in a nearest-neighbor classification.

• Journal of KIISE:Software and Applications
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• v.27 no.3
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• pp.302-313
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• 2000

In English-Korean machine translation, transitive verb-objective collocation is utilized for accurate translation of an English verbal phrase into Korean. This paper presents an algorithm for correct verb translation based on the k-nearest neighbor learning. The semantic distance is defined on the WordNet for the k-nearest neighbor learning. And we also present algorithms for automatic collocation dictionary optimization. The algorithms extract transitive verb-objective pairs as training examples from large corpora and minimize the examples, considering the tradeoff between translation accuracy and example size. Experiments show that these algorithms optimized collocation dictionary keeping about 90% accuracy for a verb 'build'.

• Journal of the Korea Society of Computer and Information
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• v.10 no.1 s.33
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• pp.85-92
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• 2005

Recently, query processing techniques for the multi-dimensional data like images have been widely used to perform content-based retrieval of the data . Range query and Nearest neighbor query are widely used multi dimensional queries . This paper Proposes the efficient pruning strategies for k-nearest neighbor query in R-tree variants indexing structures. Pruning strategy is important for the multi-dimensional indexing query processing so that search space can be reduced. We analyzed the Pruning strategies and perform experiments to show overhead and the profit of the strategies. Finally, we propose best use of the strategies.