• Title/Summary/Keyword: fuzzy clustering

Search Result 250, Processing Time 0.363 seconds

THE FUZZY CLUSTERING ALGORITHM AND SELF-ORGANIZING NEURAL NETWORKS TO IDENTIFY POTENTIALLY FAILING BANKS

  • Lee, Gi-Dong
    • 한국디지털정책학회:학술대회논문집
    • /
    • 2005.06a
    • /
    • pp.485-493
    • /
    • 2005
  • Using 1991 FDIC financial statement data, we develop fuzzy clusters of the data set. We also identify the distinctive characteristics of the fuzzy clustering algorithm and compare the closest hard-partitioning result of the fuzzy clustering algorithm with the outcomes of two self-organizing neural networks. When nine clusters are used, our analysis shows that the fuzzy clustering method distinctly groups failed and extreme performance banks from control (healthy) banks. The experimental results also show that the fuzzy clustering method and the self-organizing neural networks are promising tools in identifying potentially failing banks.

  • PDF

Simultaneous Approach to Fuzzy Clustering and Quantification of Categorical Data with Missing Values

  • Honda, Katsuhiro;Nakamura, Yoshihito;Ichihashi, Hidetomo
    • Proceedings of the Korean Institute of Intelligent Systems Conference
    • /
    • 2003.09a
    • /
    • pp.36-39
    • /
    • 2003
  • This paper proposes a simultaneous application of homogeneity analysis and fuzzy clustering with in complete data. Taking the similarity between the loss of homogeneity in homogeneity analysis and the least squares criterion in principal component analysis into account, the new objective function is defined in a similar formulation to the linear fuzzy clustering with missing values. Numerical experiment shows the characteristic properties of the proposed method.

  • PDF

A Study on the Gen Expression Data Analysis Using Fuzzy Clustering

  • Choi, Hang-Suk;Cha, Kyung-Joon;Park, Hong-Goo
    • Proceedings of the Korean Statistical Society Conference
    • /
    • 2005.05a
    • /
    • pp.25-29
    • /
    • 2005
  • Microarry 기술의 발전은 유전자의 기능과 상호 관련성 그리고 특성을 파악 가능하게 하였으며, 이를 위한 다양한 분석 기법들이 소개되고 있다. 본 연구에서 소개하는 fuzzy clustering 기법은 genome 영역의 expression 분석에 가장 널리 사용되는 기법중 비지도학습(unsupervized) 분석 기법이다. Fuzzy clustering 기법을 효모(yeast) expression 데이터를 이용하여 분류하여 hard k-means와 비교 하였다.

  • PDF

Image Segmentation Using an Extended Fuzzy Clustering Algorithm (확장된 퍼지 클러스터링 알고리즘을 이용한 영상 분할)

  • 김수환;강경진;이태원
    • Journal of the Korean Institute of Telematics and Electronics B
    • /
    • v.29B no.3
    • /
    • pp.35-46
    • /
    • 1992
  • Recently, the fuzzy theory has been adopted broadly to the applications of image processing. Especially the fuzzy clustering algorithm is adopted to image segmentation to reduce the ambiguity and the influence of noise in an image.But this needs lots of memory and execution time because of the great deal of image data. Therefore a new image segmentation algorithm is needed which reduces the memory and execution time, doesn't change the characteristices of the image, and simultaneously has the same result of image segmentation as the conventional fuzzy clustering algorithm. In this paper, for image segmentation, an extended fuzzy clustering algorithm is proposed which uses the occurence of data of the same characteristic value as the weight of the characteristic value instead of using the characteristic value directly in an image and it is proved the memory reduction and execution time reducted in comparision with the conventional fuzzy clustering algorithm in image segmentation.

  • PDF

Damage identification for high-speed railway truss arch bridge using fuzzy clustering analysis

  • Cao, Bao-Ya;Ding, You-Liang;Zhao, Han-Wei;Song, Yong-Sheng
    • Structural Monitoring and Maintenance
    • /
    • v.3 no.4
    • /
    • pp.315-333
    • /
    • 2016
  • This study aims to perform damage identification for Da-Sheng-Guan (DSG) high-speed railway truss arch bridge using fuzzy clustering analysis. Firstly, structural health monitoring (SHM) system is established for the DSG Bridge. Long-term field monitoring strain data in 8 different cases caused by high-speed trains are taken as classification reference for other unknown cases. And finite element model (FEM) of DSG Bridge is established to simulate damage cases of the bridge. Then, effectiveness of one fuzzy clustering analysis method named transitive closure method and FEM results are verified using the monitoring strain data. Three standardization methods at the first step of fuzzy clustering transitive closure method are compared: extreme difference method, maximum method and non-standard method. At last, the fuzzy clustering method is taken to identify damage with different degrees and different locations. The results show that: non-standard method is the best for the data with the same dimension at the first step of fuzzy clustering analysis. Clustering result is the best when 8 carriage and 16 carriage train in the same line are in a category. For DSG Bridge, the damage is identified when the strain mode change caused by damage is more significant than it caused by different carriages. The corresponding critical damage degree called damage threshold varies with damage location and reduces with the increase of damage locations.

Dissolved Gas Analysis of Power Transformer Using Fuzzy Clustering and Radial Basis Function Neural Network

  • Lee, J.P.;Lee, D.J.;Kim, S.S.;Ji, P.S.;Lim, J.Y.
    • Journal of Electrical Engineering and Technology
    • /
    • v.2 no.2
    • /
    • pp.157-164
    • /
    • 2007
  • Diagnosis techniques based on the dissolved gas analysis(DGA) have been developed to detect incipient faults in power transformers. Various methods exist based on DGA such as IEC, Roger, Dornenburg, and etc. However, these methods have been applied to different problems with different standards. Furthermore, it is difficult to achieve an accurate diagnosis by DGA without experienced experts. In order to resolve these drawbacks, this paper proposes a novel diagnosis method using fuzzy clustering and a radial basis neural network(RBFNN). In the neural network, fuzzy clustering is effective for selecting the efficient training data and reducing learning process time. After fuzzy clustering, the RBF neural network is developed to analyze and diagnose the state of the transformer. The proposed method measures the possibility and degree of aging as well as the faults occurred in the transformer. To demonstrate the validity of the proposed method, various experiments are performed and their results are presented.

Speaker Identification Using GMM Based on Local Fuzzy PCA (국부 퍼지 클러스터링 PCA를 갖는 GMM을 이용한 화자 식별)

  • Lee, Ki-Yong
    • Speech Sciences
    • /
    • v.10 no.4
    • /
    • pp.159-166
    • /
    • 2003
  • To reduce the high dimensionality required for training of feature vectors in speaker identification, we propose an efficient GMM based on local PCA with Fuzzy clustering. The proposed method firstly partitions the data space into several disjoint clusters by fuzzy clustering, and then performs PCA using the fuzzy covariance matrix in each cluster. Finally, the GMM for speaker is obtained from the transformed feature vectors with reduced dimension in each cluster. Compared to the conventional GMM with diagonal covariance matrix, the proposed method needs less storage and shows faster result, under the same performance.

  • PDF

Multiple Peak Detection Using the Extended Fuzzy Clustering (확장된 퍼지 클러스터링 알고리즘을 이용한 다중 첨두 검출)

  • 김수환;조창호;강경진;이태원
    • Journal of the Korean Institute of Telematics and Electronics B
    • /
    • v.29B no.1
    • /
    • pp.102-112
    • /
    • 1992
  • We have already proposed an extended fuzzy clustering algorithm which considers the importance of the data to be classified in a previous paper. In this paper, we suggest the extended fuzzy clustering algorithm based new method to slove a multiple peak detection problem, and prove experimently that this algorithm can detect the multiple peak adaptively to the noise and the shape of peaks.

  • PDF

A Study on Labeling Algorithm of ECG Signal using Fuzzy Clustering (퍼지 클러스터링을 이용한 심전도 신호의 구분 알고리즘에 관한 연구)

  • Kong, In-Wook;Kweon, Hyuk-Je;Lee, Jeong-Whan;Lee, Myoung-Ho
    • Journal of Institute of Control, Robotics and Systems
    • /
    • v.5 no.4
    • /
    • pp.427-436
    • /
    • 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.

  • PDF

Industrial load forecasting using the fuzzy clustering and wavelet transform analysis

  • Yu, In-Keun
    • Journal of IKEEE
    • /
    • v.4 no.2 s.7
    • /
    • pp.233-240
    • /
    • 2000
  • This paper presents fuzzy clustering and wavelet transform analysis based technique for the industrial hourly load forecasting fur the purpose of peak demand control. Firstly, one year of historical load data were sorted and clustered into several groups using fuzzy clustering and then wavelet transform is adopted using the Biorthogonal mother wavelet in order to forecast the peak load of one hour ahead. The 5-level decomposition of the daily industrial load curve is implemented to consider the weather sensitive component of loads effectively. The wavelet coefficients associated with certain frequency and time localization is adjusted using the conventional multiple regression method and the components are reconstructed to predict the final loads through a five-scale synthesis technique. The outcome of the study clearly indicates that the proposed composite model of fuzzy clustering and wavelet transform approach can be used as an attractive and effective means for the industrial hourly peak load forecasting.

  • PDF