• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.3
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• pp.180-185
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• 2015

Digital infrared thermal imaging is a non-invasive adjunctive diagnostic technique that allows an examiner to visualize and quantify changes in skin surface temperature. The asymmetry of temperature differences between the diseased and the contralateral healthy body parts can be automatically analyzed and has been studied in many areas of medical science. In this paper, we propose a method for intelligent automatic asymmetry detection based on a K-means analysis and a YCbCr color model. The implemented software successfully visualizes an asymmetric distribution of colors with respect to the patients’ health status.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.13-18
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• 2018

In this study, we classify Seoul metro stations according to boarding and alighting patterns using machine earning technique. The target data is the number of boarding and alighting passengers per hour every day at 233 subway stations from 2008 to 2017 provided by the public data portal. Gaussian mixture model (GMM) and K-means clustering are used as machine learning techniques in order to classify subway stations. The distribution of the boarding time and the alighting time of the passengers can be modeled by the Gaussian mixture model. K-means clustering algorithm is used for unsupervised learning based on the data obtained by GMM modeling. As a result of the research, Seoul metro stations are classified into four groups according to boarding and alighting patterns. The results of this study can be utilized as a basic knowledge for analyzing the characteristics of Seoul subway stations and analyzing it economically, socially and culturally. The method of this research can be applied to public data and big data in areas requiring clustering.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.43-50
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• 2010

Fuzzy c-means (FCM) and possibilistic c-means (PCM) are the two most well-known clustering algorithms in fuzzy clustering area, and have been applied in many applications in their original or modified forms. However, FCM's noise sensitivity problem and PCM's overlapping cluster problem are also well known. Recently there have been several attempts to combine both of them to mitigate the problems and possibilistic fuzzy c-means (PFCM) showed promising results. In this paper, we proposed a modified PFCM using regularization to reduce noise sensitivity in PFCM further. Regularization is a well-known technique to make a solution space smooth and an algorithm noise insensitive. The proposed algorithm, PFCM with regularization (PFCM-R), can take advantage of regularization and further reduce the effect of noise. Experimental results are given and show that the proposed method is better than the existing methods in noisy conditions.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.572-579
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• 2003

With the expansion of e-commerce, E-marketplace has become one of the most discussed topics in recent years. Limited theoretical works, however, have been done to optimize the practical use of e-marketplace systems. Other potential issues aside, this research has focused on this problem: 'the participants waste too much time, effort and cost to find out their best partner in B2B marketplace.' To solve this problem, this paper proposes a system which provides the user-company with the automated and customized brokering service. The system proposed in this paper assesses the weight on the priorities of a user-company, runs the two-stage clustering algorithm with self-organizing map and K-means clustering technique. Subsequently, the system shows the clustering result and user guide-line. This system enables B2B marketplace to have more efficiency on transaction with smaller pool of partners to be searched.

• The Journal of the Acoustical Society of Korea
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• v.6 no.4
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• pp.44-54
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• 1987

In this paper, the problem of LP-model and it's solution by liftering in cepstrum domain are investigated in speaker independent isolated-word recognition. And, clustering technique is discussed for obtaining the reference template. KMA (K-means iteration with average) method, which is transformed from UWA method and K-iteration method, has been suggested and compared with each other for clustering, the result of recognition experiments shows max. $95\%$ recognition rate when rasied-sign lifter and KMA clustering method is applied.

• Structural Monitoring and Maintenance
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• v.2 no.4
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• pp.339-355
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• 2015

Corrosion of prestressed concrete structures is one of the main challenges that engineers face today. In response to this national need, this paper presents the results of a long-term project that aims at developing a structural health monitoring (SHM) technology for the nondestructive evaluation of prestressed structures. In this paper, the use of permanently installed low profile piezoelectric transducers (PZT) is proposed in order to record the acoustic emissions (AE) along the length of the strand. The results of an accelerated corrosion test are presented and k-means clustering is applied via principal component analysis (PCA) of AE features to provide an accurate diagnosis of the strand health. The proposed approach shows good correlation between acoustic emissions features and strand failure. Moreover, a clustering technique for the identification of false alarms is proposed.

• Communications for Statistical Applications and Methods
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• v.20 no.2
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• pp.97-105
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• 2013

Stratification (among other applications) is a popular technique used in survey practice to improve the accuracy of estimators. Its full potential benefit can be gained by the effective use of auxiliary variables in stratification related to survey variables. This paper focuses on the problem of stratum formation when multiple stratification variables are available. We first review a variance reduction strategy in the case of univariate stratification. We then discuss its use for multivariate situations in convenient and efficient ways using three methods: compromised measures of size, principal components analysis and a K-means clustering algorithm. We also consider three types of compromising factors to data when using these three methods. Finally, we compare their efficiency using data from MU281 Swedish municipality population.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.183-188
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• 2013

A variety of cluster analysis techniques prerequisite to cluster objects having similar characteristics in data mining. But the clustering of those algorithms have lots of difficulties in dealing with categorical data within the databases. The imprecise handling of uncertainty within categorical data in the clustering process stems from the only algebraic logic of rough set, resulting in the degradation of stability and effectiveness. This paper proposes a information-theoretic rough entropy(RE) by taking into account the dependency of attributes and proposes a technique called min-mean-mean roughness(MMMR) for selecting clustering attribute. We analyze and compare the performance of the proposed technique with K-means, fuzzy techniques and other standard deviation roughness methods based on ZOO dataset. The results verify the better performance of the proposed approach.

• Journal of Industrial Technology
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• v.29 no.B
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• pp.9-15
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• 2009

In the case of traditional binary encoding technique, it takes long time to converge the optimal solutions and brings about complexity of the systems due to encoding and decoding procedures. However, the ROGAs (real-coded genetic algorithms) do not require these procedures, and the k-means clustering algorithm can avoid global searching space. Thus, this paper proposes a new approach by using their advantages. The proposed method constructs the multiple predictors using the optimal differences that can reveal the patterns better and properties concealed in non-stationary time series where the k-means clustering algorithm is used for data classification to each predictor, then selects the best predictor. After selecting the best predictor, the cluster centers of the predictor are tuned finely via RCGKA in secondary tuning procedure. Therefore, performance of the predictor can be more enhanced. Finally, we verifies the prediction performance of the proposed system via simulating typical time series examples.

• Fashion & Textile Research Journal
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• v.23 no.4
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• pp.480-490
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• 2021

Sales forecasting is crucial for many retail operations. For apparel retailers, accurate sales forecast for the next season is critical to properly manage inventory and plan their supply chains. The challenge in this increases because apparel products are always new for the next season, have numerous variations, short life cycles, long lead times, and seasonal trends. In this study, a sales forecasting model is proposed for apparel products using machine learning techniques. The sales data pertaining to outerwear items for four years were collected from a Korean sports brand and filtered with outliers. Subsequently, the data were standardized by removing the effects of exogenous variables. The sales patterns of outerwear items were clustered by applying K-means clustering, and outerwear attributes associated with the specific sales-pattern type were determined by using a decision tree classifier. Six types of sales pattern clusters were derived and classified using a hybrid model of clustering and decision tree algorithm, and finally, the relationship between outerwear attributes and sales patterns was revealed. Each sales pattern can be used to predict stock-keeping-unit-level sales based on item attributes.