• IEIE Transactions on Smart Processing and Computing
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• v.5 no.6
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• pp.430-439
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• 2016

Estimation and analysis of traffic jams plays a vital role in an intelligent transportation system and advances safety in the transportation system as well as mobility and optimization of environmental impact. For these reasons, many researchers currently mainly focus on the brilliant machine learning-based prediction approaches for traffic prediction systems. This paper primarily addresses the analysis and comparison of prediction accuracy between two machine learning algorithms: Naïve Bayes and K-Nearest Neighbor (K-NN). Based on the fact that optimized estimation accuracy of these methods mainly depends on a large amount of recounted data and that they require much time to compute the same function heuristically for each action, we propose an approach that applies multi-threading to these heuristic methods. It is obvious that the greater the amount of historical data, the more processing time is necessary. For a real-time system, operational response time is vital, and the proposed system also focuses on the time complexity cost as well as computational complexity. It is experimentally confirmed that K-NN does much better than Naïve Bayes, not only in prediction accuracy but also in processing time. Multi-threading-based K-NN could compute four times faster than classical K-NN, whereas multi-threading-based Naïve Bayes could process only twice as fast as classical Bayes.

• Journal of the Korean Society for information Management
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• v.23 no.4 s.62
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• pp.215-231
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• 2006

Recently there are many bibliometric studies attempting to utilize Pathfinder networks(PFNets) for examining and analyzing the intellectual structure of a scholarly field. Pathfinder network scaling has many advantages over traditional multidimensional scaling, including its ability to represent local details as well as global intellectual structure. However there are some limitations in PFNets including very high time complexity. And Pathfinder network scaling cannot be combined with cluster analysis, which has been combined well with traditional multidimensional scaling method. In this paper, a new method named as Parallel Nearest Neighbor Clustering (PNNC) are proposed for complementing those weak points of PFNets. Comparing the clustering performance with traditional hierarchical agglomerative clustering methods shows that PNNC is not only a complement to PFNets but also a fast and powerful clustering method for organizing informations.

• Journal of Korea Spatial Information System Society
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• v.11 no.3
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• pp.9-18
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• 2009

Recently, due to the advanced technologies of mobile devices and wireless communication, there are many studies on telematics and LBS(location-based service) applications. because moving objects usually move on spatial networks, their locations are updated frequently, leading to the degradation of retrieval performance. To manage the frequent updates of moving objects' locations in an efficient way, a new distributed grid scheme, called DS-GRID (distributed S-GRID), and k-NN(k-nearest neighbor) query processing algorithm was proposed[1]. However, the result of k-NN query processing technique may be invalidated as the location of query and moving objects are changed. Therefore, it is necessary to study on continuous k-NN query processing algorithm. In this paper, we propose both MCE-CKNN and MBP(Monitoring in Border Point)-CKNN algorithmss are S-GRID. The MCE-CKNN algorithm splits a query route into sub-routes based on cell and seproves retrieval performance by processing query in parallel way by. In addition, the MBP-CKNN algorithm stores POIs from the border points of each grid cells and seproves retrieval performance by decreasing the number of accesses to the adjacent cells. Finally, it is shown from the performance analysis that our CKNN algorithms achieves 15-53% better retrieval performance than the Kolahdouzan's algorithm.

• The Korean Journal of Applied Statistics
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• v.31 no.3
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• pp.417-426
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• 2018

An alternative developmental toxicity test using mouse embryonic stem cell derived embryoid bodies has been developed. This alternative method is not to administer chemicals to animals, but to treat chemicals with cells. This study suggests the use of Discriminant Analysis, Support Vector Machine, Artificial Neural Network and k-Nearest Neighbor. Algorithm performance was compared with accuracy and a weighted Cohen's kappa coefficient. In application, various classification techniques were applied to cytotoxicity data to classify drug toxicity and compare the results.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.611-620
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• 2021

The maintenance industry is mainly progressing based on condition-based maintenance after corrective maintenance and preventive maintenance. In condition-based maintenance, maintenance is performed at the optimum time based on the condition of equipment. In order to find the optimal maintenance point, it is important to accurately understand the condition of the equipment, especially the remaining useful life. Thus, using simulation data (C-MAPSS), a prediction model is proposed to predict the remaining useful life of a turbofan engine. For the modeling process, a C-MAPSS dataset was preprocessed, transformed, and predicted. Data pre-processing was performed through piecewise RUL, moving average filters, and standardization. The remaining useful life was predicted using principal component analysis and the k-NN method. In order to derive the optimal performance, the number of principal components and the number of neighbor data for the k-NN method were determined through 5-fold cross validation. The validity of the prediction results was analyzed through a scoring function while considering the usefulness of prior prediction and the incompatibility of post prediction. In addition, the usefulness of the RUL prediction model was proven through comparison with the prediction performance of other neural network-based algorithms.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.28.1-28.1
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• 2021

We present a statistical analysis of dark matter halos with interacting neighbors using a set of cosmological simulations. We classify the neighbors into two groups based on the total energy (E₁₂) of the target-neighbor system; flybying neighbors (E₁₂ ≥ 0) and merging ones (E₁₂ < 0). First, we find a different trend between the flyby and merger fractions in terms of the halo mass and large-scale density. The flyby fraction highly depends on the halo mass and environment, while the merger fraction show little dependence. Second, we measure the spin-orbit alignment, which is the angular alignment between the spin of a target halo (${\vec{S}}$ ) and the orbital angular momentum of its neighbor (${\vec{L}}$). In the spin-orbit angle distribution, the flybying neighbors show a weaker prograde alignment with their target halos than the merging neighbors do. With respect to the nearest filament, the flybying neighbor has a behavior different from that of the merging neighbor. Finally, we discuss the physical origin of two interaction types.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.223-224
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• 2021

Recently, k-farthest neighbor (kFN) queries have not as much attention as k-nearest neighbor (kNN) queries. Therefore, this study considers moving k-farthest neighbor (MkFN) queries for spatial network databases. Given a positive integer k, a moving query point q, and a set of data points P, MkFN queries can constantly retrieve k data points that are farthest from the query point q. The challenge with processing MkFN queries in spatial networks is to avoid unnecessary or superfluous distance calculations between the query and associated data points. This study proposes a batch processing algorithm, called MOFA, to enable efficient processing of MkFN queries in spatial networks. MOFA aims to avoid dispensable distance computations based on the clustering of both query and data points. Moreover, a time complexity analysis is presented to clarify the effect of the clustering method on the query processing time. Extensive experiments using real-world roadmaps demonstrated the efficiency and scalability of the MOFA when compared with a conventional solution.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.145-150
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• 2015

Needs about safety of residents are important in urbanized society, elderly and small-size family. People are looking for safety information system and device of CPTED. That is, Needs and Installations of CCTV increased steadily. But, scientific analysis about validity, systematic plan and location of security CCTV is nonexistent. It is simply put these devised in more demanded areas. It has limits to look for safety of residents by increasing density of CCTVs. One of the characteristics of crime is clustering and stong interconnectivity. So, exploratory spatial data of crime is geo-coded using 2 years data and carried out cluster analysis and space statistical analysis through GIS space analysis by dividing 18 variables into social economy, urban space, crime prevention facility and crime occurrence index. The result of analysis shows cluster of 5 major crimes, theft, violence and sexual violence by Nearest Neighbor distance analysis and Ripley's K function. It also shows strong crime interconnectivity through criminal correlation analysis. In case of finding criminal cluster, you can find criminal hotspot. So, in this study I found concept of hotspot and considered technique about selection of hotspot. And then, selected hotspot about 5 major crimes, theft, violence and sexual violence through Nearest Neighbor Hierarchical Spatial Clustering.

• Journal of KIISE
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• v.42 no.11
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• pp.1303-1313
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• 2015

MapReduce provides high levels of system scalability and fault tolerance for large-size data processing. A MapReduce-based k-nearest-neighbor(k-NN) join algorithm seeks to produce the k nearest-neighbors of each point of a dataset from another dataset. The algorithm has been considered important in bigdata analysis. However, the existing k-NN join query-processing algorithm suffers from a high index-construction cost that makes it unsuitable for the processing of bigdata. To solve the corresponding problems, we propose a new grid-based, k-NN join query-processing algorithm. Our algorithm retrieves only the neighboring data from a query cell and sends them to each MapReduce task, making it possible to improve the overhead data transmission and computation. Our performance analysis shows that our algorithm outperforms the existing scheme by up to seven-fold in terms of the query-processing time, while also achieving high extent of query-result accuracy.