Comparison of clustering algorithms for analog modulation classification

Güldemir, Hanifi; Şengür, Abdulkadir

doi:10.1016/j.eswa.2005.07.014

Cited by 53 publications

(25 citation statements)

References 20 publications

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“…Therefore, an automatic selecting method of suitable THV ranges to segment singular cardiac cycle of parameters was used secondly by the aid of so-called Fuzzy C-means clustering method (Bezdek, Hathaway, Sabin, & Tucker, 1992;Bezdek & Pal, 1992;Guldemir & Sengur, 2006;Hammouda, 2000;Jiang & Choi, 2006). This process is shown in Fig.…”

Section: T1mentioning

confidence: 99%

Comparison of envelope extraction algorithms for cardiac sound signal segmentation

Choi

Jiang

2008

Expert Systems with Applications

198

104

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Section: T1mentioning

confidence: 99%

Comparison of envelope extraction algorithms for cardiac sound signal segmentation

Choi

Jiang

2008

Expert Systems with Applications

198

104

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“…This initial set of clusters' centers does not represent the optimal clustering condition. Next, FCM computes and assigns each data point with a membership degree of each cluster [30]. The computation is performed based on Eq.…”

Section: B Fuzzy C-meansmentioning

confidence: 99%

Experimental study on the effective range of FCM's fuzzifier values for web services' QoS data

Hasan

Jaafar

Hassan

2014

2014 International Conference on Computer and Information Sciences (ICCOINS)

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The work presented in this paper is part of the development of a fuzzy Interval Type-2 (IT2)-based system. An IT2 system contains fuzzy type-2 membership functions that can be generated using a pair of FCM fuzzifier, m values. Evidences show that the effective range of m values is influenced by the underlying dataset. Hence, the objective of this paper is to present the experimental study on finding the effective range of m values for web services' QoS data. The study aimed at identifying range of m values that successfully generated Gaussian membership functions. As proposed by previous work, the experiment was carried out upon the m values in the range of 1.4 to 2.6. The works involved the datasets of three QoS parameters. The results showed that two of the datasets has the effective range of 1.7 to 2.6, while another one was 1.6 to 2.6.

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“…Common clustering methods include traditional hierarchy clustering, K-means clustering, Fuzzy c-means clustering, mountain clustering and subtractive clustering [21]. Hierarchy clustering uses inconsistency coefficient threshold to find the cluster number and its members.…”

Section: Clustering Featurementioning

confidence: 99%

Recognition of wear mode using multi-variable synthesis approach based on wavelet packet and improved three-line method

2007

Mechanical Systems and Signal Processing

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Comparison of clustering algorithms for analog modulation classification

Cited by 53 publications

References 20 publications

Comparison of envelope extraction algorithms for cardiac sound signal segmentation

Comparison of envelope extraction algorithms for cardiac sound signal segmentation

Experimental study on the effective range of FCM's fuzzifier values for web services' QoS data

Recognition of wear mode using multi-variable synthesis approach based on wavelet packet and improved three-line method

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