A Comparative Study of Classical Clustering Method and Cuckoo Search Approach for Satellite Image Clustering: Application to Water Body Extraction

Labed, Kaouter; Fizazi, Hadria; Mahi, Habib; Galván, Inés María

doi:10.1080/08839514.2018.1451214

Cited by 9 publications

(2 citation statements)

References 35 publications

(36 reference statements)

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“…[1,18,19,31,32,109] Square sum function of the error Internal Measure the quality of cluster either by compactness or homogeneity. [12,23,111] Entropy External Measure mutual information based on the probability distribution of random variables. [30,112,113] F-measure External…”

Section: Measurements Categories Usage Referencesmentioning

confidence: 99%

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

et al. 2019

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Clustering techniques can group genes based on similarity in biological functions. However, the drawback of using clustering techniques is the inability to identify an optimal number of potential clusters beforehand. Several existing optimization techniques can address the issue. Besides, clustering validation can predict the possible number of potential clusters and hence increase the chances of identifying biologically informative genes. This paper reviews and provides examples of existing methods for clustering genes, optimization of the objective function, and clustering validation. Clustering techniques can be categorized into partitioning, hierarchical, grid-based, and density-based techniques. We also highlight the advantages and the disadvantages of each category. To optimize the objective function, here we introduce the swarm intelligence technique and compare the performances of other methods. Moreover, we discuss the differences of measurements between internal and external criteria to validate a cluster quality. We also investigate the performance of several clustering techniques by applying them on a leukemia dataset. The results show that grid-based clustering techniques provide better classification accuracy; however, partitioning clustering techniques are superior in identifying prognostic markers of leukemia. Therefore, this review suggests combining clustering techniques such as CLIQUE and k-means to yield high-quality gene clusters.

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Section: Measurements Categories Usage Referencesmentioning

confidence: 99%

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

et al. 2019

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“…Nevertheless, several evaluation criteria have been developed in the literature to measure quantitatively the quality of a clustering. In this work, we proposed the WB validity index [39,40]. In comparison to other indices [41,42], a low value of this index implies simplicity and high clustering quality.…”

Section: The Wb Indexmentioning

confidence: 99%

Multiple classifier system for remotely sensed data clustering

Ramdane

Mahi

Daho

et al. 2021

IET Image Processing

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The Multiple Classifier System (or classifier ensemble) is the consensus of different clustering algorithms that can provide high accuracy for the best partition and thus overcome the constraints of conventional approaches based on single classifiers. The MCS is divided into two stages: Partition creation and partition combining. The potential benefits of this methodology in unsupervised land cover categorization utilizing synthetic, composite, and remotely sensed data are investigated in this paper. Four clustering algorithms are used for the MCS's first step, and according to the WB index, the best‐unsupervised classification is obtained. In the second stage, relabeling and, voting approaches are then applied. The MCS's experimental results outperform the individual clustering outcomes in terms of accuracy.

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Critical review on deep learning methodologies employed for water-body segmentation through remote sensing images

Gautam

Singhai

2023

Multimed Tools Appl

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A Comparative Study of Classical Clustering Method and Cuckoo Search Approach for Satellite Image Clustering: Application to Water Body Extraction

Cited by 9 publications

References 35 publications

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

Multiple classifier system for remotely sensed data clustering

Critical review on deep learning methodologies employed for water-body segmentation through remote sensing images

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