K-Means Cloning: Adaptive Spherical K-Means Clustering

Hedar, Abdel-Rahman; Ibrahim, Abdelmonem M.; Abdel-Hakim, Alaa E.; Sewisy, Adel A.

doi:10.3390/a11100151

Cited by 12 publications

(7 citation statements)

References 70 publications

(78 reference statements)

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“…A similar observation can be done for the fitness function CF κ [48]. Hence, the selected metaheuristics have to be set up for a maximisation problem.…”

Section: The Initialisation Phasementioning

confidence: 82%

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A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

et al. 2019

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This article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structure using deterministic clustering methods and stochastic optimisation approaches to optimally centre the clusters. Similar to other state-of-the-art methods available in the literature, it uses "microclusters" and other established techniques, such as density based clustering. Unlike other methods, it makes use of metaheuristic optimisation to maximise performances during the initialisation phase, which precedes the classic online phase. Experimental results show that OpStream outperforms the state-of-the-art methods in several cases, and it is always competitive against other comparison algorithms regardless of the chosen optimisation method. Three variants of OpStream, each coming with a different optimisation algorithm, are presented in this study. A thorough sensitive analysis is performed by using the best variant to point out OpStream's robustness to noise and resiliency to parameter changes.

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“…A similar observation can be done for the fitness function CF κ [48]. Hence, the selected metaheuristics have to be set up for a maximisation problem.…”

Section: The Initialisation Phasementioning

confidence: 82%

“…The formulation of the cost function plays a key part. In this research, the "Cluster Fitness" (CF) function from [48] was chosen as its maximisation leads to a high intra-cluster distance, which is desirable. Its mathematical formulation, for the κth (κ = 1, 2, 3, .…”

Section: The Initialisation Phasementioning

confidence: 99%

A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

et al. 2019

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show abstract

“…The algorithm is simple, the process is fast and works based on the proximity of the distance between the elements to the center of the cluster, to produce as many as K cluster data [37]. Distance calculations are carried out on various properties that form the basis for grouping [38]. With a predetermined number of clusters as many as K and a randomly assigned initial cluster center, pixels are grouped on the basis of their proximity.…”

Section: D) Clustering Based Methodsmentioning

confidence: 99%

Image Segmentation Based on Color Dissimilarity

Karma¹,

Putra

Sudarma

et al. 2022

IJCAI

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This study aims to develop a segmentation technique that can be used to detect objects in an image. The concept used is to imitate the human concept of recognizing an object based on its color difference. A color is considered different if it has different R, G or B values. Humans can only distinguish two colors clearly if they both have a DeltaE value of at least 8. The difference in DeltaE values is obtained from differences in the values of R, G or B, either alone or in pairs. The application of this concept to the segmentation technique has shown good results.This technique is able to give the same results and is good for images with JPG, PNG and BMP file types. To provide confidence in the performance of this method, a comparison is made with several segmentation methods. By measuring the similarity index using the Jaccard index, Sørensen-Dice index and BF Score, this color dissimilarity-based segmentation method has the best performance. Meanwhile, when measuring the processing time, this method has the second best performance. Thus, it can be concluded that this method is very suitable for detecting objects in an image. Povzetek: Tehnika segmentacije je bila razvita z imitacijo človeškega koncepta prepoznavanja predmeta, in sicer na podlagi barvnih razlik. Barve se štejejo za različne, če imajo vrednost DeltaE najmanj 8, ki jo dobimo iz razlik v vrednostih R, G ali B barv samih ali v parih. Izvajanje te tehnike kaže enake rezultate za datoteke JPG, PNG in BMP in je v veliko pomoč pri postopku identifikacije predmeta.

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“…Dari uraian penelitian terkait di atas dapat disimpulkan bahwa dalam pengelompokkan kasus perceraian dapat diterapkan pada kasus lain dengan atribut yang berbeda sehingga pada penelitian ini akan dilakukan teknik pengelompokan data perceraian berdasarkan desa dengan dengan atribut yang digunakan yaitu Laki-laki (talak) dan perempuan (gugat) metode yang digunakan yaitu teknik data mining clustering. Teknik data mining clustering akan mengelompokkan data dengan karakteristik yang sama [8] dan mengelompokkan data dengan karakteristik yang berbeda ke kelompok lain [9]. Metode yang digunakan dalam penelitian ini yaitu dengan menggunakan algoritma K-Means yaitu mengelompokkan data berdasarkan titik pusat klaster atau centroid terdekat dengan data [10].…”

Section: Pendahuluanunclassified

Analisis Algoritma K-Means dan Davies Bouldin Index dalam Mencari Cluster Terbaik Kasus Perceraian di Kabupaten Kuningan

Sopyan¹,

Lesmana²,

Juliane³

2022

bits

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In marriage, the thing that is most avoided is a divorce. Divorce is the termination of the husband and wife relationship which is carried out legally at the time of trial. From year to year, there is an increase in the number of divorces in Indonesia, including the number of divorces in Kuningan Regency. This study analyzes divorce cases in villages in Kuningan Regency, the analysis is carried out by using data mining clustering methods using the K-Means algorithm. The clustering method is grouping data based on the same characteristics. In determining the number of clusters by using the value of the smallest Davies Bouldin Index, it is hoped that the number of clusters formed can be more optimal. The results of this study are that there are 4 clusters consisting of villages or sub-districts with different divorce rates, namely the highest divorce rate, high divorce rate, medium divorce rate, low divorce rate, and lowest divorce rate

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K-Means Cloning: Adaptive Spherical K-Means Clustering

Cited by 12 publications

References 70 publications

A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

Image Segmentation Based on Color Dissimilarity

Analisis Algoritma K-Means dan Davies Bouldin Index dalam Mencari Cluster Terbaik Kasus Perceraian di Kabupaten Kuningan

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