Fuzzy clustering based on -nearest-neighbours rule

Zerhouni, Noureddine; Abouelala, O.; Limouri, M.; Essaid, A.

doi:10.1016/s0165-0114(99)00074-3

Cited by 46 publications

(22 citation statements)

References 15 publications

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“…The concept of neighbourhood similarity is also taken into account. The clusters resulting from density-based methods may be of arbitrarily shapes [3,4].…”

Section: Clusteringmentioning

confidence: 99%

Generation of a Hybrid Clustering Algorithm for Big Data

Ahlawat¹

2018

ijarcs

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Abstract:In this paper, a Hybrid Algorithm for clustering big data is proposed which is based on Rank Similarity. Rank Similarity is calculated by taking the sum of both Cosine and Gaussian Similarity. Proposed Technique is compared with the existing technique which is based on Cosine Similarity only. Comparison is done by taking parameters precision, recall, F-Measure, and accuracy. Results are evaluated on Java Netbeans 8.2.

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“…The concept of neighbourhood similarity is also taken into account. The clusters resulting from density-based methods may be of arbitrarily shapes [3,4].…”

Section: Clusteringmentioning

confidence: 99%

Generation of a Hybrid Clustering Algorithm for Big Data

Ahlawat¹

2018

ijarcs

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“…Note that the fact that the fuzzy k-nearest neighbor rule is not feasible does not invalidate the method in [23]. That method relies on a completely different approach, which is actually prototype-based as it combines fuzzy k-nearest neighbor and (classical) fuzzy c-means.…”

Section: Fuzzy K-nearest Neighbormentioning

confidence: 99%

Prototype-less Fuzzy Clustering

Borgelt

2007

2007 IEEE International Fuzzy Systems Conference

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Abstract-In contrast to standard fuzzy clustering, which optimizes a set of prototypes, one for each cluster, this paper studies fuzzy clustering without prototypes. Starting from an objective function that only involves the distances between data points and the membership degrees of the data points to the different clusters, an iterative update rule is derived. The properties of the resulting algorithm are then examined, especially w.r.t. to schemes that focus on a constrained neighborhood for each data point. Corresponding experimental results are reported that demonstrate the merits of this approach.

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“…Moreover, it contains many variables which are too vague to model. In order to build an accurate model for the pumping station system, several algorithms based on Takagi-Sugeno (T-S) fuzzy model [1][2][3][4][5][6][7] have been carried out recently to identify the parameters for "black-box" systems using input-output data sets, among them the Fuzzy-C Means (FCM) algorithm [8][9][10][11][12][13][14][15][16]. The latter is particularly the most effective technique that can be used in nonlinear systems identification.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Supervisor Approach Design Based‐Switching Controller for Pumping Station: Experimental Validation

Chakchouk¹,

Regaya²,

Zaafouri³

et al. 2017

Mathematical Problems in Engineering

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This paper proposes a discrete-time switching controller strategy for a hydraulic process pumping station. The proposed solution leads to improving control system performances with two tests: combination of Fuzzy-PD and PI controllers and Fuzzy-PID and PI controllers. The proposed design methodology is based on accurate model for pumping station (PS), which is developed in previous works using Fuzzy-C Means (FCM) algorithm. The control law design is based on switching control; a fuzzy supervisor manages the switching from one to another and regulates the rate of participation of each order, in order to satisfy various objectives of a stable pumping station like the asymptotic stability of the tracking error. To validate the proposed solution, experimental tests are made and analyzed. Compared to the conventional PI and fuzzy logic (FL) approaches, the results show that the switching controller allows exhibiting excellent transient response over a wide range of operating conditions and especially is easier to be implemented in practice.

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Fuzzy clustering based on -nearest-neighbours rule

Cited by 46 publications

References 15 publications

Generation of a Hybrid Clustering Algorithm for Big Data

Generation of a Hybrid Clustering Algorithm for Big Data

Prototype-less Fuzzy Clustering

Fuzzy Supervisor Approach Design Based‐Switching Controller for Pumping Station: Experimental Validation

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