• Title/Summary/Keyword: Fuzzy C-Means

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A Novel Approach towards use of Adaptive Multiple Kernels in Interval Type-2 Possibilistic Fuzzy C-Means (적응적 Multiple Kernels을 이용한 Interval Type-2 Possibilistic Fuzzy C-Means 방법)

  • Joo, Won-Hee;Rhee, Frank Chung-Hoon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.24 no.5
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    • pp.529-535
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    • 2014
  • In this paper, we propose a hybrid approach towards multiple kernels interval type-2 possibilistic fuzzy C-means(PFCM) based on interval type-2 possibilistic fuzzy c-means(IT2PFCM) and possibilistic fuzzy c-means using multiple kernels( PFCM-MK). In case of noisy data or overlapping cluster prototypes, fuzzy C-means gives poor performance in comparison to possibilistic fuzzy C-means(PFCM). Moreover, to address the uncertainty associated with fuzzifier parameter m, interval type-2 possibilistic fuzzy C-means(PFCM) is used. Most of the practical data available are complex and non-linearly separable. In such cases using Gaussian kernels proves helpful. Therefore, in order to overcome all these issues, we have integrated multiple kernels possibilistic fuzzy C-means(PFCM) into interval type-2 possibilistic fuzzy C-means(IT2PFCM) and propose the idea of multiple kernels based interval type-2 possibilistic fuzzy C-means(IT2PFCM-MK).

The Effect of Variable Learning Weights in Fuzzy c-means algorithm (Fuzzy c-means 알고리즘에서의 가변학습 가중치의 효과)

  • 박소희;조제황
    • Proceedings of the Korea Institute of Convergence Signal Processing
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    • 2001.06a
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    • pp.109-112
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    • 2001
  • 기존의 K-means 알고리즘은 학습벡터가 단일군집에 할당되는 방법이 crisp 이므로 다른 군집에 할당될 확률을 무시하게 된다. 따라서 군집화 작업과 관련하여 반복적인 코드북 설계 과정에서 각 학습벡터를 다중 군집으로 할당하는 Fuzzy c-means를 사용한다. 또한 Fuzzy c-means 알고리즘의 학습과정에서 구해지는 각 클래스 의 프로토타입에 가중치를 곱하여 다음 학습의 프로토타입으로 사용함으로써 Fuzzy c-means 알고리즘 적용 결과 얻어지는 코트북의 성능을 기존 알고리즘과 비교하여 개선된 Fuzzy c-means 알고리즘을 찾기 위한 근거를 마련한다.

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Improvement on Fuzzy C-Means Using Principal Component Analysis

  • Choi, Hang-Suk;Cha, Kyung-Joon
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.2
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    • pp.301-309
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    • 2006
  • In this paper, we show the improved fuzzy c-means clustering method. To improve, we use the double clustering as principal component analysis from objects which is located on common region of more than two clusters. In addition we use the degree of membership (probability) of fuzzy c-means which is the advantage. From simulation result, we find some improvement of accuracy in data of the probability 0.7 exterior and interior of overlapped area.

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Noise resistant density based Fuzzy C-means Clustering Algorithm (노이즈에 강한 밀도를 이용한 Fuzzy C-means 클러스터링 알고리즘)

  • Go, Jeong-Won;Choe, Byeong-In;Lee, Jeong-Hun
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2006.11a
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    • pp.211-214
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    • 2006
  • Fuzzy C-Means(FCM) 알고리즘은 probabilitic 멤버쉽을 사용하는 클러스터링 방법으로서 널리 쓰이고 있다. 하지만 이 방법은 노이즈에 대하여 민감한 성질을 가진다는 단점이 있다. 따라서 본 논문에서는 이러한 노이즈에 민감한 성질을 보완하기 위해서 데이터의 밀도추정을 이용하여 새로운 FCM 알고리즘을 제안한다. 본 논문에서 제안된 알고리즘은 FCM과 비슷한 성능의 클러스터링 수행이 가능하며, 노이즈가 포함된 데이터에서는 FCM보다 더 나은 성능을 보여준다.

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A Density Estimation based Fuzzy C-means Algorithm for Image Segmentation (영상분할을 위한 밀도추정 바탕의 Fuzzy C-means 알고리즘)

  • Ko, Jeong-Won;Choi, Byung-In;Rhee, Frank Chung-Hoon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.17 no.2
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    • pp.196-201
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    • 2007
  • The Fuzzy E-means (FCM) algorithm is a widely used clustering method that incorporates probabilitic memberships. Due to these memberships, it can be sensitive to noise data. In this paper, we propose a new fuzzy C-means clustering algorithm by incorporating the Parzen Window method to include density information of the data. Several experimental results show that our proposed density-based FCM algorithm outperforms conventional FCM especially for data with noise and it is not sensitive to initial cluster centers.

Design of Radial Basis Function with the Aid of Fuzzy KNN and Conditional FCM (퍼지 kNN과 Conditional FCM을 이용한 퍼지 RBF의 설계)

  • Roh, Seok-Beon;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.58 no.6
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    • pp.1223-1229
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    • 2009
  • The performance of Radial Basis Function Neural Networks depends on setting up the Radial Basis Functions over the input space which are the important design procedure of Radial Basis Function Neural Networks. The existing method to initialize the location of the radial basis functions over the input space is to use the conditional fuzzy C-means clustering. However, the researchers which are interested in the conditional fuzzy C-means clustering cannot get as good modeling performance as they expect because the conditional fuzzy C-means clustering cannot project the information which is extracted over the output space into the input space. To compensate the above mentioned drawback of the conditional fuzzy C-means clustering, we apply a fuzzy K-nearest neighbors approach to project the auxiliary information defined over the output space into the input space without lose of the information.

Clustering of Incomplete Data Using Autoencoder and fuzzy c-Means Algorithm (AutoEncoder와 FCM을 이용한 불완전한 데이터의 군집화)

  • 박동철;장병근
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.29 no.5C
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    • pp.700-705
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    • 2004
  • Clustering of incomplete data using the Autoencoder and the Fuzzy c-Means(PCM) is proposed in this paper. The Proposed algorithm, called Optimal Completion Autoencoder Fuzzy c-Means(OCAEFCM), utilizes the Autoencoder Neural Network (AENN) and the Gradiant-based FCM (GBFCM) for optimal completion of missing data and clustering of the reconstructed data. The proposed OCAEFCM is applied to the IRIS data and a data set from a financial institution to evaluate the performance. When compared with the existing Optimal Completion Strategy FCM (OCSFCM), the OCAEFCM shows 18%-20% improvement of performance over OCSFCM.

Segmentation of Color Image by Subtractive and Gravity Fuzzy C-means Clustering (차감 및 중력 fuzzy C-means 클러스터링을 이용한 칼라 영상 분할에 관한 연구)

  • Jin, Young-Goun;Kim, Tae-Gyun
    • Journal of IKEEE
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    • v.1 no.1 s.1
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    • pp.93-100
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    • 1997
  • In general, fuzzy C-means clustering method was used on the segmentation of true color image. However, this method requires number of clusters as an input. In this study, we suggest new method that uses subtractive and gravity fuzzy C-means clustering. We get number of clusters and initial cluster centers by applying subtractive clustering on color image. After coarse segmentation of the image, we apply gravity fuzzy C-means for optimizing segmentation of the image. We show efficiency of the proposed algorithm by qualitative evaluation.

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A Fast K-means and Fuzzy-c-means Algorithms using Adaptively Initialization (적응적인 초기치 설정을 이용한 Fast K-means 및 Frizzy-c-means 알고리즘)

  • 강지혜;김성수
    • Journal of KIISE:Software and Applications
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    • v.31 no.4
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    • pp.516-524
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    • 2004
  • In this paper, the initial value problem in clustering using K-means or Fuzzy-c-means is considered to reduce the number of iterations. Conventionally the initial values in clustering using K-means or Fuzzy-c-means are chosen randomly, which sometimes brings the results that the process of clustering converges to undesired center points. The choice of intial value has been one of the well-known subjects to be solved. The system of clustering using K-means or Fuzzy-c-means is sensitive to the choice of intial values. As an approach to the problem, the uniform partitioning method is employed to extract the optimal initial point for each clustering of data. Experimental results are presented to demonstrate the superiority of the proposed method, which reduces the number of iterations for the central points of clustering groups.

Modeling and Classification of MPEG VBR Video Data using Gradient-based Fuzzy c_means with Divergence Measure (분산 기반의 Gradient Based Fuzzy c-means 에 의한 MPEG VBR 비디오 데이터의 모델링과 분류)

  • 박동철;김봉주
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.29 no.7C
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    • pp.931-936
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    • 2004
  • GBFCM(DM), Gradient-based Fuzzy c-means with Divergence Measure, for efficient clustering of GPDF(Gaussian Probability Density Function) in MPEG VBR video data modeling is proposed in this paper. The proposed GBFCM(DM) is based on GBFCM( Gradient-based Fuzzy c-means) with the Divergence for its distance measure. In this paper, sets of real-time MPEG VBR Video traffic data are considered. Each of 12 frames MPEG VBR Video data are first transformed to 12-dimensional data for modeling and the transformed 12-dimensional data are Pass through the proposed GBFCM(DM) for classification. The GBFCM(DM) is compared with conventional FCM and GBFCM algorithms. The results show that the GBFCM(DM) gives 5∼15% improvement in False Alarm Rate over conventional algorithms such as FCM and GBFCM.