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Multi-Objective Optimized Fuzzy Clustering for Detecting Cell Clusters from Single-Cell Expression Profiles
2019
Genes
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We proposed a multi-objective optimization-based fuzzy clustering approach for detecting cell clusters from scRNA-seq data. First, we conducted initial filtering and SCnorm normalization. ...
Next, we set the first measure as minimization objective (↓) and the remaining three as maximization objectives (↑), and then applied a multi-objective decision-making technique, TOPSIS, to identify the ...
Our main objective behind our study is to propose a new method to identify cell clusters using multi-objective fuzzy clustering technique for single-cell data. ...
doi:10.3390/genes10080611
pmid:31412637
pmcid:PMC6723724
fatcat:5txs6xiksfcyjc5wgiwh6sddnu
An Integrated Color Image Segmentation with Multi-class SVM followed by SRFCM
2019
International journal of recent technology and engineering
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With the help of SR-FCM (Soft Rough Fuzzy-C-Means) clustering. Membership functions based on the fuzzy set are facing the major problem of cluster overlapping. ...
texture with multi-class SVM (Support Vector Machine).For color feature extraction we used homogeneity model and for textural features we used PLD (Power Law Descriptor). ...
Segmented Color Image by Proposed Work Here an integrated technique for color image segmentation which uses Multi-SVM classifier followed by Soft Rough Fuzzy-C-Means clustering for segmentation. ...
doi:10.35940/ijrte.b1114.0882s819
fatcat:cq46zk7zwrbidl3baplaelwskq
A Survey: Image Segmentation Techniques
2014
International Journal of Future Computer and Communication
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It divides a digital image into multiple regions in order to analyze them. It is also used to distinguish different objects in the image. ...
This paper presents a literature review of basic image segmentation techniques from last five years. Recent research in each of image segmentation technique is presented in this paper. ...
Proposed technique overcomes Spectral Clustering method. Gang Chen [42] found that fast extraction of object information from a given image is still a problem for real time image processing. ...
doi:10.7763/ijfcc.2014.v3.274
fatcat:r2xrjqhddvf25crozekx6z4psy
An Archive-based Steady-State Fuzzy Differential Evolutionary Algorithm for Data Clustering (ASFDEaDC)
2021
Journal of Information Technology Management
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In the current paper, we have assimilated fuzzy techniques and optimization techniques, namely differential evolution, to put forward a modern archive-based fuzzy evolutionary algorithm for multi-objective ...
The current work account for the application of a cluster associated approach. ...
An archive-based steady-state fuzzy differential evolutionary algorithm for data clustering In this paper, the proposed algorithm is a fuzzy evolutionary optimization algorithm based on clustering. ...
doi:10.22059/jitm.2021.80617
doaj:9ce171fbcdb241d59dbcc4e973b37b15
fatcat:77nbqycemnda3exrpl7nlie3de
Aggregation of multi-objective fuzzy symmetry-based clustering techniques for improving gene and cancer classification
2017
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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Firstly, some multiobjective-based fuzzy clustering techniques are developed using the search capabilities of differential evolution and particle swarm optimization. ...
Both these clustering techniques utilize a recently developed point symmetrybased distance for allocation of points to different clusters. ...
Fuzzy symmetry with modified differential evolution-based multi-objective cluster ensemble method (EDE) The classical differential evolution (DE) (Storn and Price 1997) is a recently devised heuristic ...
doi:10.1007/s00500-017-2865-3
fatcat:o44edi5u5vhxjcqmbydvnqfdxy
AN EFFICIENTFUZZY C-MEANS CLUSTERING ALGORITHM FOR MULTI-VALUED DATA SETS
2021
Information Technology in Industry
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The proposed algorithm is a partition clustering strategy that uses fuzzy c- means clustering for evolutions, which is using the novel member ship function by utilizing the proposed similarity measure. ...
In data analysis, items were mostly described by a set of characteristics called features, in which each feature contains only single value for each object. ...
[3] suggested a clustering technique for setvalued data called SV-k-modes algorithm here the similarity measure for the two objects with multi-valued attributes is defined and a set-valued mode interpretation ...
doi:10.17762/itii.v9i1.265
fatcat:peintnypwngj7phgvagc2y3574
Spatial continuity incorporated multi-attribute fuzzy clustering algorithm for blood vessels segmentation
2010
Science China Information Sciences
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Spatial continuity incorporated multi-attribute fuzzy clustering algorithm for blood vessels segmentation. ...
In this paper, a spatial continuity incorporated multi-attribute fuzzy clustering algorithm (MAFCM S) is proposed to segment entire blood vessels from TOF MRA images. ...
S = 2 λ 2 1 + λ 2 2 + λ 2 3 , (3) where R A is essential for distinguishing between plate-like and line-like structures, R B differentiates sheet-like objects from other structures. ...
doi:10.1007/s11432-010-0072-2
fatcat:nkaect6jbnd4xp7xwxqsi4qnr4
A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications
2020
Applied Sciences
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, fuzzy clustering, image processing, and wireless sensor networks. ...
The Harmony Search Algorithm (HSA) is a swarm intelligence optimization algorithm which has been successfully applied to a broad range of clustering applications, including data clustering, text clustering ...
Feng et al., in [94] , introduced a fuzzy kernel clustering technique with a new differential HSA to manage the classification process. ...
doi:10.3390/app10113827
fatcat:okeokml755b2dcx6yjamnhtml4
Multi-grid cellular genetic algorithm for optimizing variable ordering of ROBDDs
2012
2012 IEEE Congress on Evolutionary Computation
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This paper presents a cellular genetic algorithm for optimizing the variable order in Reduced Ordered Binary Decision Diagrams. The evolution process is inspired by a basic genetic algorithm. ...
The population evolves on a bidimensional grid and is implicitly organized in geographical clusters that present a form of structural similarity between individuals. ...
Parks, A Preliminary Study of a new Multiobjective Optimization Algorithm 311, Vui Ann Shim, Kay Chen Tan and Kok Kiong Tan, A Hybrid Estimation of Distribution Algorithm for Solving the Multi-objective ...
doi:10.1109/cec.2012.6256590
dblp:conf/cec/RotaruB12
fatcat:4ly3nrktw5habc6lf5err7d5py
Colour Image Segmentation Using Soft Rough Fuzzy-C-Means and Multi Class SVM
2016
International Journal on Cybernetics & Informatics
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The Multi Class SVM is trained using the samples obtained from Soft Rough Fuzzy-C-Means (SRFCM) clustering. Fuzzy set based membership functions capably handle the problem of overlapping clusters. ...
In this paper, a color image segmentation algorithm is proposed by extracting both texture and color features and applying them to the One-Against-All Multi Class Support Vector Machine classifier for ...
For image pixels in j th cluster some pixels are chosen as training samples remaining are used as test samples. 4. Multi SVM pixel classification Apply the test set to SVM for classifying new data. ...
doi:10.5121/ijci.2016.5425
fatcat:hu2rotkehbedlkm4mdz5t5y44a
Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
2021
Applied Sciences
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The current clustering approaches are categorized into meta-heuristic, fuzzy logic, and hybrid based on the network organization and adopted clustering management techniques. ...
The role of the regular nodes in a clustered WSN is to sense the environment and transmit the sensed data to the selected head node; this CH gathers the data for onward forwarding to the Base Station. ...
• DFLBCHSA: A distributed fuzzy clustering algorithm for a WSN with a mobile gateway The multi-objective distributed fuzzy clustering (DFLBCHSA) approach was developed to reduce delay in delivering ...
doi:10.3390/app112311448
fatcat:4qv3evyfonetxf626liogvihzi
Automatic Clustering Using a Synergy of Genetic Algorithm and Multi-objective Differential Evolution
[chapter]
2009
Lecture Notes in Computer Science
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This paper applies the Differential Evolution (DE) and Genetic Algorithm (GA) to the task of automatic fuzzy clustering in a Multi-objective Optimization (MO) framework. ...
A real-coded representation of the search variables, accommodating variable number of cluster centers, is used for GADE. ...
Multi-objective Clustering Scheme
Search-variable Representation and Description of the new algorithm In the proposed method, for n data points, each d-dimensional, and for a userspecified maximum number ...
doi:10.1007/978-3-642-02319-4_21
fatcat:6xkrm4w37jgy3mizapzwmrcq4e
A Systematic Assessment of Numerical Association Rule Mining Methods
2021
SN Computer Science
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Different authors have presented various algorithms for each numerical association rule mining method; therefore, it is hard to select a suitable algorithm for a numerical association rule mining task. ...
In data mining, the classical association rule mining techniques deal with binary attributes; however, real-world data have a variety of attributes (numerical, categorical, Boolean). ...
Alatas (2008) Based on multi-objective differential evolutionary algorithm. ARM-DE [20] I. ...
doi:10.1007/s42979-021-00725-2
fatcat:yhtda5qukrgf3hwmso6w4sh5fe
Classification of Countries based on MacroEconomic Variables using Fuzzy Support Vector Machine
2011
International Journal of Computer Applications
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FUZZY C-MEANS CLUSTERING Clustering is unsupervised classification techniques that classifies or groups the objects into different-2 subset, in each subset all the object have same type of properties. ...
The paper is organized as follows: In the II section we discuss about the fuzzy c-means clustering and section III deals with SVM Multi-classification techniques. ...
doi:10.5120/3302-4513
fatcat:r2no77bryffgddpdbs6t65h64m
Image Segmentation Techniques: A Survey
2014
Journal of Image and Graphics
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Existing segmentation techniques can't satisfy all type of images. This survey addressed various image segmentation techniques, evaluates them and presents the issues related to those techniques. ...
Image segmentation is a mechanism used to divide an image into multiple segments. It will make image smooth and easy to evaluate. ...
Results have shown for a zebra image as correct segmentation. Yong-mei Zhou [22] has introduced new region-based image segmentation technique with the help of mean-shift clustering algorithm. ...
doi:10.12720/joig.1.4.166-170
fatcat:aqn3vpnpejdaloz3r6u7sbohri
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