• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.428-432
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• 2007

We present a novel method for determining k nearest neighbors, which accurately recognizes the underlying clusters in a data set. To this end, we introduce the "tiling neighborhood" which is constructed by tiling a number of small local circles rather than a single circle, as existing neighborhood schemes do. Then we formulate the problem of determining the tiling neighborhood as a minimax optimization, leading to an efficient message passing algorithm. For several real data sets, our method outperformed the k-nearest neighbor method. The results suggest that our method can be an alternative to existing for general classification tasks, especially for data sets which have many missing values.

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.17-27
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• 2010

Feature selection and feature weighting are useful techniques for improving the classification accuracy of k-Nearest Neighbor (k-NN) classifier. The main propose of feature selection and feature weighting is to reduce the number of features, by eliminating irrelevant and redundant features, while simultaneously maintaining or enhancing classification accuracy. In this paper, a novel hybrid approach is proposed for simultaneous feature selection, feature weighting and choice of k in k-NN classifier based on Genetic Algorithm. The results have indicated that the proposed algorithm is quite comparable with and superior to existing classifiers with or without feature selection and feature weighting capability.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.505-513
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• 2017

Recently, numerous small area estimation studies have been conducted to obtain more detailed and accurate estimation results. Most of these studies have employed spatial regression models, which require a clear definition of spatial neighborhoods. In this study, we introduce the Delaunay triangulation as a method to define spatial neighborhood, and compare this method with the k-nearest neighbor method. A simulation was conducted to determine which of the two methods is more efficient in defining spatial neighborhood, and we demonstrate the performance of the proposed method using a land price data.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.6
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• pp.1873-1879
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• 2014

There are various methodologies to forecast the travel time using real-time data but the K-nearest neighborhood (KNN) method in general is regarded as the most one in forecasting when there are enough historical data. The objective of this study is to evaluate applicability of KNN method. In this study, real-time and historical data of toll collection system (TCS) traffic flow and the dedicated short range communication (DSRC) link travel time, and the historical path travel time data are used as input data for KNN approach. The proposed method investigates the path travel time which is the nearest to TCS traffic flow and DSRC link travel time from real-time and historical data, then it calculates the predicted path travel time using weight average method. The results show that accuracy increased when weighted value of DSRC link travel time increases. Moreover the trend of forecasted and real travel times are similar. In addition, the error in forecasted travel time could be further reduced when more historical data could be available in the future database.

• Journal of Korea Multimedia Society
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• v.20 no.1
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• pp.74-81
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• 2017

In learning stage of the preliminary Cut-off indoor positioning scheme, RSSI and UUID data received from beacons at each reference point(RP) are stored in fingerprint map. The fingerprint map and real-time beacon information are compared to identify the nearest K reference points through which the user position is estimated. If the number of K is zero, this scheme cannot estimate user position. We have improved the preliminary Cut-off scheme to get the estimated user position even in the case. The improved scheme excludes the beacon of the weakest signal received by user mobile device and identifies neighborhood reference points using the other beacon information. This procedure are performed repetitively until K > 0. The simulation results confirm that the proposed scheme outperforms K-Nearest-Neighbor (KNN), Cluster KNN and the conventional Cut-off scheme in terms of accuracy while the constraints are guaranteed to be satisfied.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.101-111
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• 2006

One of the most widely used method of handling missingness in microarray data is the kNN(k Nearest Neighborhood) method. Recently Li and Gui (2004) suggested, so called PCR(Partial Cox Regression) method which deals with censored survival times and microarray data efficiently via kNN imputation method. In this article, we try to show that the way to treat missingness eventually affects the further statistical analysis.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.616-619
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• 2007

Nowadays, urbanized area expands its boundary, and distribution of urbanized area is gradually transformed into more complicated pattern. In Korea, SMA(Seoul Metropolitan Area) has outstanding urbanized area since 1950s. But it is ambiguous whether urban distribution is clustered or dispersed. This study aims to show the way in which expansion of urbanized area impacts on spatial distribution pattern of urbanized area. We use quadrat analysis, nearest-neighbor analysis and fractal analysis to know distribution pattern of urbanized area in time-series urban growth. The quadrat analysis indicates that distribution pattern of urbanized area is clustered but the cohesion is gradually weakened. And the nearest-neighbor analysis shows that point patterns are changed that urbanized area distribution pattern is progressively changed from clustered pattern into dispersed pattern. The fractal dimension analysis shows that 1972's distribution dimension is 1.428 and 2000's dimension is 1.777. Therefore, as time goes by, the complexity of urbanized area is more increased through the years. As a result, we can show that the cohesion of the urbanized area is weakened and complicated.

• The Journal of the Korea Contents Association
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• v.12 no.8
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• pp.47-58
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• 2012

In this paper, we propose a continuous k-nearest neighborhood query processing method for updating the query results in real-time over mobile peer-to-peer environments. The proposed method disseminates a monitoring region to efficiently monitor the k-nearest neighbor peers. The Monitoring Region is created to assure at least k peers as the result of the query within the time range using the vector of neighbor peers. In the propose method, the monitoring region is valid for a long time because it is calculated by the vector of neighbor peers of the query peer. Therefore, the proposed method decreases the cost of re-processing by monitoring region invalidation. In order to show the superiority of the proposed method, we compare it with the previous schemes through performance evaluation.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.621-625
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• 2010

This paper presents a hand motion tracking method for hand gesture interface using a camera in mobile devices such as a smart phone and PDA. When a camera moves according to the hand gesture of the user, global optical flows are generated. Therefore, robust hand movement estimation is possible by considering dominant optical flow based on histogram analysis of the motion direction. A continuous hand gesture is segmented into unit gestures by motion state estimation using motion phase, which is determined by velocity and acceleration of the estimated hand motion. Feature vectors are extracted during movement states and hand gestures are recognized at the end state of each gesture. Support vector machine (SVM), k-nearest neighborhood classifier, and normal Bayes classifier are used for classification. SVM shows 82% recognition rate for 14 hand gestures.