• Journal of Institute of Control, Robotics and Systems
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• v.13 no.1
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• pp.46-51
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• 2007

In this paper, we propose a new data clustering method using local probability and hypothesis theory. To cluster the test data set we analyze the local area of the test data set using local probability distribution and decide the candidate class of the data set using mean standard deviation and variance etc. To decide each class of the test data, statistical hypothesis theory is applied to the decided candidate class of the test data set. For evaluating, the proposed classification method is compared to the conventional fuzzy c-mean method, k-means algorithm and Discriminator analysis algorithm. The simulation results show more accuracy than results of fuzzy c-mean method, k-means algorithm and Discriminator analysis algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.431-433
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• 2007

In This paper, we present a texture clustering method to extract Container ISO code in complex images. First, we make texture informations using top-hat morphology from realtime images, and we cluster those informations using horizontal and vertical clustering method to extract text area. After extensive experiment, our method demonstrated superior performance against well-known techniques as texture and histogram method.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.624-627
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• 2005

This paper presents a new data classification method using the Robbins Monro stochastic approximation algorithm k-nearest neighbor and distribution analysis. To cluster the data set, we decide the centroid of the test data set using k-nearest neighbor algorithm and the local area of data set. To decide each class of the data, the Robbins Monro stochastic approximation algorithm is applied to the decided local area of the data set. To evaluate the performance, the proposed classification method is compared to the conventional fuzzy c-mean method and k-nn algorithm. The simulation results show that the proposed method is more accurate than fuzzy c-mean method, k-nn algorithm and discriminant analysis algorithm.

• 한국정보디스플레이학회:학술대회논문집
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• 2007.08a
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• pp.1001-1004
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• 2007

This paper proposes a novel RGB-LED (light emitting diode) backlight system, for 32" LCD TVs, accompanied by a new X-Y Channel driving method in which its row and column switches control the individual division screen. This proposed driving method is able to produce division driving effects such as image improvement and reduced power consumption. Not only that, the number of driver needed in this method, that is 3 power supplies with 3*(m+n) switches, is much fewer than that of cluster driving method, that is 3*(m*n) driver.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.4
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• pp.591-600
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• 1998

The scheduling of parallel computing systems consists of two procedures, the assignment of tasks to each available processor and the ordering of tasks in each processor. The assignment procedure is same with a clustering. The clustering is classified into linear or nonlinear according to the precedence relationship of the tasks in each cluster. The parallel computing system can be modeled with a Directed Acyclic Graph(DAG). By the granularity theory, DAG is categorized into Coarse Grain Type(CDAG) and Fine Grain Type(FDAG). We suggest the linear clustering method for the scheduling of CDAG using the genetic algorithm. The method utilizes a properly that the optimal schedule of a CDAG is one of linear clustering. We present the computational comparisons between the suggested method for CDAG and an existing method for the general DAG including CDAG and FDAG.

• Proceedings of the KIPE Conference
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• 2007.07a
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• pp.334-336
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• 2007

This paper proposes a novel RGB-LED (light emitting diode) backlight system, for 32" LCD TVs, accompanied by a new X-Y Channel drive method in which its row and column switches control the individual division screen. This proposed driving method is able to produce division drive effects such as image improvement and reduced power consumption. Not only that, the number of converter needed in this method, that is 1 with $4^*$(m+n) switches, is much fewer than that of cluster drive method, that is $4^*(m^*n)$.

• IE interfaces
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• v.19 no.1
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• pp.86-96
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• 2006

Some customer data of manufacturing industries have a large number of incomplete data set due to the customer's infrequent purchasing behavior and the limitation of customer profile data gathered from sales representatives. So that, most sophisticated data analysis methods may not be applied directly. This paper proposes a heuristic data analysis method to classify customers in household appliances industries. The proposed PD (percent of difference) method can be used for the discriminant analysis of incomplete customer data with simple mathematical calculations. The method is composed of variable distribution estimation step, PD measure and cluster score evaluation steps, variable impact construction step, and segment assignment step. A real example is also presented.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.795-808
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• 2020

Due to the poor evaluation indexes such as detection accuracy and recall rate when Yolov3 network detects aircraft in remote sensing images, in this paper, we propose a remote sensing image aircraft detection method based on machine vision. In order to improve the target detection effect, the Inception module was introduced into the Yolov3 network structure, and then the data set was cluster analyzed using the k-means algorithm. In order to obtain the best aircraft detection model, on the basis of our proposed method, we adjusted the network parameters in the pre-training model and improved the resolution of the input image. Finally, our method adopted multi-scale training model. In this paper, we used remote sensing aircraft dataset of RSOD-Dataset to do experiments, and finally proved that our method improved some evaluation indicators. The experiment of this paper proves that our method also has good detection and recognition ability in other ground objects.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.1-11
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• 2023

Clustering analysis is used in various fields including customer segmentation and clustering methods such as k-means are actively applied in the credit card customer segmentation. In this paper, we summarized the input features selection method of k-means clustering for the case of the credit card customer segmentation problem, and evaluated its feasibility through the analysis results. By using the label values of k-means clustering results as target features of a decision tree classification, we composed a method for prioritizing input features using the information gain of the branch. It is not easy to determine effectiveness with the clustering effectiveness index, but in the case of the CH index, cluster effectiveness is improved evidently in the method presented in this paper compared to the case of randomly determining priorities. The suggested method can be used for effectiveness of actively used clustering analysis including k-means method.

• Journal of Nutrition and Health
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• v.53 no.1
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• pp.39-53
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• 2020

Purpose: The purpose of the study was to examine the snack meal management for infants and toddlers and the demand for snack products according to mothers' sustainable dietary style in the Jeonbuk area. Methods: The participants were 359 mothers in the Jeonbuk area. The data was analyzed using factor analysis, cluster analysis, analysis of variance, and χ²-tests with SPSS v. 25.0. According to the sustainable dietary style, the situation of providing snacks at home, the purchasing behavior for snack products, and the satisfaction and, demand for snack products were investigated. Results: Using the K-clustering method, the sustainable dietary style of the subjects was categorized into 3 clusters. Cluster 1 was the family health-seeking group, cluster 2 was the sustainable dietary trend group, and cluster 3 was the sustainability-interested group. The frequency of snack intakes according to the cluster groups showed a significant difference (p < 0.001). Fruits were the snack item most frequently consumed by all the cluster groups. Approximately 92.8% of mothers had purchased snack products, and 95.2% of the subjects were satisfied with them (p < 0.05). The main reason for satisfaction with the snack products in all the cluster groups was the various kinds of products with considering the growth stage of children. Clusters 2 and 3 required the development of snack products using organic food materials, while cluster 1 required the snack products to be supplemented with various nutrients. Conclusion: It is necessary to develop various snack products according to the sustainable dietary style and the needs of mothers so that these snacks can increase the satisfaction of mothers with the snack products and lead them to better snack purchasing behavior.