• Journal of Institute of Control, Robotics and Systems
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• v.5 no.4
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• pp.427-436
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• 1999

This paper describes an ECG signal labeling algorithm based on fuzzy clustering, which is very useful to the automated ECG diagnosis. The existing labeling methods compares the crosscorrelations of each wave form using IF-THEN binary logic, which tends to recognize the same wave forms such as different things when the wave forms have a little morphological variation. To prevent this error, we have proposed as ECG signal labeling algorithm using fuzzy clustering. The center and the membership function of a cluster is calculated by a cluster validity function. The dominant cluster type is determined by RR interval, and the representative beat of each cluster is determined by MF (Membership Function). The problem of IF-THEN binary logic is solved by FCM (Fuzzy C-Means). The MF and the result of FCM can be effectively used in the automated fuzzy inference -ECG diagnosis.

• Journal of the Korea Society of Computer and Information
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• v.17 no.12
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• pp.83-93
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• 2012

This paper proposes an image segmentation framework that modifies the objective function of Fuzzy C-Means (FCM) to improve the performance and computational efficiency of the conventional FCM-based image segmentation. The proposed image segmentation framework includes a locally weighted fuzzy c-means (LWFCM) algorithm that takes into account the influence of neighboring pixels on the center pixel by assigning weights to the neighbors. Distance between a center pixel and a neighboring pixels are calculated within a window and these are basis for determining weights to indicate the importance of the memberships as well as to improve the clustering performance. We analyzed the segmentation performance of the proposed method by utilizing four eminent cluster validity functions such as partition coefficient ($V_{pc}$), partition entropy ($V_{pe}$), Xie-Bdni function ($V_{xb}$) and Fukuyama-Sugeno function ($V_{fs}$). Experimental results show that the proposed LWFCM outperforms other FCM algorithms (FCM, modified FCM, and spatial FCM, FCM with locally weighted information, fast generation FCM) in the cluster validity functions as well as both compactness and separation.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.3
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• pp.81-88
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• 1996

Cluster analysis is based on partitioning a collection of data points into a number of clusters, where the data points in side a cluster have a certain degree of similarity and it is a fundamental process of data analysis. So, it has been playing an important role in solving many problems in pattern recognition and image processing. For these many clustering algorithms depending on distance criteria have been developed and fuzzy set theory has been introduced to reflect the description of real data, where boundaries might be fuzzy. If fuzzy cluster analysis is tomake a significant contribution to engineering applications, much more attention must be paid to fundamental questions of cluster validity problem which is how well it has identified the structure that is present in the data. Several validity functionals such as partition coefficient, claasification entropy and proportion exponent, have been used for measuring validity mathematically. But the issue of cluster validity involves complex aspects, it is difficult to measure it with one measuring function as the conventional study. In this paper, we propose four performance indices and the way to measure the quality of clustering formed by given learning strategy.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.7
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• pp.51-60
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• 1997

Prototype based methods are commonly used in cluster analysis and the results may be highly dependent on the prototype used. In this paper, we propose a fuzzy clustering method that involves adaptively expanding convex polytopes. Thus, the dependency on the use of prototypes can be eliminated. The proposed method makes it possible to effectively represent an arbitrarily distributed data set without a priori knowledge of the number of clusters in the data set. Specifically, nonlinear membership functions are utilized to determine whether a new cluster is created or which vertex of the cluster should be expanded. For this, the membership function of a new vertex is assigned according to not only a distance measure between an incoming pattern vector and a current vertex, but also the amount how much the current vertex has been modified. Therefore, cluster expansion can be only allowed for one cluster per incoming pattern. Several experimental results are given to show the validity of our mehtod.

• Korea Journal of Hospital Management
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• v.28 no.2
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• pp.9-20
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• 2023

Purpose: This study classified the actual functions of geriatric hospitals and examined the differences in their characteristics, in order to provide a basis for discussions on defining the functions of geriatric hospitals and how to pay for care. Methodology: This study used various administrative data such as health insurance data and long-term care insurance data. Cluster analysis was used to categorize geriatric hospitals. To examine the validity of the cluster analysis results, we conducted a discriminant analysis to calculate the accuracy of the classification. To examine cluster characteristics, we examined structure, process, and outcome indicators for each cluster. Findings: The cluster analysis identified five clusters. They were geriatric hospitals with relatively short stays for cancer patients(cluster 1; cancer patient-centered), geriatric hospitals with relatively large numbers of patients using rehabilitation services(cluster 2; rehabilitation patient-centered), geriatric hospitals with a high proportion of relatively severe elderly patients(cluster 3; severe elderly patient-centered), geriatric hospitals with a high proportion of mildly ill elderly patients with various conditions(cluster 4; mildly ill elderly patient-centered), and geriatric hospitals with a significantly higher proportion of dementia patients(cluster 5; dementia patient-centered). The largest number of geriatric hospitals were categorized in clusters 4 and 5, and the structure and process indicators for these clusters were generally lower than for the other clusters. Practical Implications: We have confirmed the existence of geriatric hospitals where the medical function, which is the original purpose of a geriatric hospital, has been weakened. It has been observed that the quality level of these geriatric hospitals is likely to be lower compared to hospitals that prioritize enhanced medical functions. Therefore, it is suggested to consider the conversion of these geriatric hospitals into long-term care facilities, and careful consideration should be given to the review of care-giver payment coverage.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.541-546
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• 2006

Data Mining is the process of finding hidden patterns inside a large data set. Cluster analysis has been used as a popular technique for data mining. It is a fundamental process of data analysis and it has been Playing an important role in solving many problems in pattern recognition and image processing. If fuzzy cluster analysis is to make a significant contribution to engineering applications, much more attention must be paid to fundamental decision on the number of clusters in data. It is related to cluster validity problem which is how well it has identified the structure that Is present in the data. In this paper, we design an adaptive data mining model using fuzzy performance measures. It discovers clusters through an unsupervised neural network model based on a fuzzy objective function and evaluates clustering results by a fuzzy performance measure. We also present the experimental results on newsgroup data. They show that the proposed model can be used as a document classifier.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.767-778
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• 2003

This paper presents a method for generating optimal fuzzy rules using a genetic algorithm. Fuzzy rules are generated from the training data in the first stage. In this stage, fuzzy c-Means clustering method and cluster validity are used to determine the structure and initial parameters of the fuzzy inference system. A cluster validity is used to determine the number of clusters, which can be the number of fuzzy rules. Once the structure is figured out in the first stage, parameters relating the fuzzy rules are optimized in the second stage. Weights and variance parameters are tuned using genetic algorithms. Variance parameters are also managed with left and right for asymmetrical Gaussian membership function. The method ensures convergence toward a global minimum by using genetic algorithms in weight and variance spaces.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.285-292
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• 2000

In general, exploratory data analysis consists of three processes: i) assessment of clustering tendency, ii) cluster analysis, and iii) cluster validation. This analysis method requiring a number of iterations of step ii) and iii) to converge is computationally inefficient. In this paper, we propose a density function-based approximate fuzzy clustering method with a hierachical structure which consosts of two phases: Phase I is a features(i.e., number of clusters and cluster centers) extraction process based on the tendency assessment of a given data and Phase II is a standard FCM with the cluster centers intialized by the results of the Phase I. Numerical examples are presented to show the validity of the proposed clustering method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.121-124
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• 2000

We introduce an advanced mountain clustering method which uses a normalized data space, a gaussian type mountain function and a deconstruction method using mountain slope. This is more useful than Yagers mountain method because it needs just one parameter to tune instead of three and finds out more resonable cluster centers. Computational examples are presented to show the validity of the advanced mountain method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.5B
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• pp.557-565
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• 2011

In this paper, we assume that sensor nodes organize the multi-hop networks, are fixed, and operate as full function devices(FFD). The wireless sensor network(WSN) only consists of mobile nodes without the assistance from the fixed infrastructure, which increases the flexibility of the network. However, it is difficult to perform routing in the WSN, since sensor nodes freely join in and drop out of the network, and some sensor nodes have very low power. We propose the algorithm combining routing schemes based on the bitmap and cluster methods in this paper. Through computer simulations, we show the validity of the proposed algorithm.