Design of Novel Metaheuristic Techniques for Clustering

Moussa, Dina A.; Eissa, Noureldin S.; Abounaser, Hassan; Badr, Amr

doi:10.1109/access.2018.2882471

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…As GA is a type of stochastic probabilistic search technique, statistical validity methods were used to validate the precision of the experiments. The algorithm's performance was also compared with that of the more recent metaheuristic Immuno-Genetic Algorithm (IGA) proposed by Moussa et al [33]. It was tested the same number of times (five) using the same unified data sets.…”

Section: Resultsmentioning

confidence: 99%

A Metaheuristic Technique for Cluster-Based Feature Selection of DNA Methylation Data for Cancer

Eissa¹,

Khairuddin²,

Yusof³

et al. 2023

Computers, Materials &Amp; Continua

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Epigenetics is the study of phenotypic variations that do not alter DNA sequences. Cancer epigenetics has grown rapidly over the past few years as epigenetic alterations exist in all human cancers. One of these alterations is DNA methylation; an epigenetic process that regulates gene expression and often occurs at tumor suppressor gene loci in cancer. Therefore, studying this methylation process may shed light on different gene functions that cannot otherwise be interpreted using the changes that occur in DNA sequences. Currently, microarray technologies; such as Illumina Infinium BeadChip assays; are used to study DNA methylation at an extremely large number of varying loci. At each DNA methylation site, a beta value (β) is used to reflect the methylation intensity. Therefore, clustering this data from various types of cancers may lead to the discovery of large partitions that can help objectively classify different types of cancers as well as identify the relevant loci without user bias. This study proposed a Nested Big Data Clustering Genetic Algorithm (NBDC-GA); a novel evolutionary metaheuristic technique that can perform cluster-based feature selection based on the DNA methylation sites. The efficacy of the NBDC-GA was tested using real-world data sets retrieved from The Cancer Genome Atlas (TCGA); a cancer genomics program created by the National Cancer Institute (NCI) and the National Human Genome Research Institute. The performance of the NBDC-GA was then compared with that of a recently developed metaheuristic Immuno-Genetic Algorithm (IGA) that was tested using the same data sets. The NBDC-GA outperformed the IGA in terms of convergence performance. Furthermore, the NBDC-GA produced a more robust clustering configuration while simultaneously decreasing the dimensionality of features to a maximum of 67% and of 94.5% for individual cancer type and collective cancer, respectively. The proposed NBDC-GA was also able to identify two chromosomes with highly contrasting DNA methylations activities that were previously linked to cancer.

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Section: Resultsmentioning

confidence: 99%

A Metaheuristic Technique for Cluster-Based Feature Selection of DNA Methylation Data for Cancer

Eissa¹,

Khairuddin²,

Yusof³

et al. 2023

Computers, Materials &Amp; Continua

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“…Later, Dina and her colleagues proposed metaheuristic techniques that determine the number of clusters for a variable length chromosome [31]. Genetic algorithm, artificial immune system, and a hybrid between both, namely, IGA were applied on five real-world data sets.…”

Section: Related Workmentioning

confidence: 99%

Automatic Clustering of DNA Sequences With Intelligent Techniques

2021

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With the discovery of new DNAs, a fundamental problem arising is how to categorize those DNA sequences into correct species. Unfortunately, identifying all data groups correctly and assigning a set of DNAs into k clusters where k must be predefined are one of the major drawbacks in clustering analysis, especially when the data have many dimensions and the number of clusters is too large and hard to guess. Furthermore, finding a similarity measure that preserves the functionality and represents both the composition and distribution of the bases in a DNA sequence is one of the main challenges in computational biology. In this paper, a new soft computing metaheuristic framework is introduced for automatic clustering to generate the optimal cluster formation and to determine the best estimate for the number of clusters. Pulse coupled neural network (PCNN) is utilized for the calculation of DNA sequence similarity or dissimilarity. Bat algorithm is hybridized with the well-known genetic algorithm to solve the automatic data clustering problem. Extensive computational experiments are conducted on the expanded human oral microbiome database (eHOMD). A comparative study between the experimental results shows that the proposed hybrid algorithm achieved superior performance over the standard genetic algorithm and bat algorithm. Moreover, the hybrid performance was compared with competing algorithms from the literature review to ascertain its superiority. Mann-Whitney-Wilcoxon rank-sum test is conducted to statistically validate the obtained clusters.

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“…All selected nodes complete from the first point. v. Iterate the above process, until all points are found to form a cluster[14]. Ordering Points To Identify the Clustering Structure") Density-based search algorithm groups in local data.…”

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confidence: 99%

“…Presented by Michael Encrust, Marcus M. Brewing, Hans Peter Craigie, and J Jr rg Sander. Its basic idea is similar to DBSCAN[14]. OPTICS is also a density-based algorithm based on connectivity and it is 1.6 times faster than DBSCAN.CACTUSCACTUS stands for Clustering..Categorical..Data Using Summaries.…”

mentioning

confidence: 99%

Systematic Review: A State of Art ML Based Clustering Algorithms for Data Mining

Ali

Faheem

Waseem

et al. 2020

2020 IEEE 23rd International Multitopic Conference (INMIC)

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Data mining is an unsupervised learning technique to extract the insights and hidden relationships among data. Data mining has more importance in data science and machine learning because through data mining all hidden information is shown to determine various aspects of the data set. Clustering is a data mining technique to group the data, on the basis of similarity measures. The objects or data points in a cluster are similar. Similarly, objects or data points in another cluster will also be similar. But when these clusters are compared, they are dissimilar to each other. Clustering is considered the most important unsupervised learning technique because it deals with finding a structure in a collection of unlabeled data. Clustering can be done by the different approaches like partitioning clustering, hierarchical clustering, density-based clustering, and grid-based clustering. These clustering approaches can be done by the numbers of algorithms, such as K-means clustering, Fuzzy C-means clustering, Hierarchical clustering, DBSCAN, OPTICS, STING, ROCK and CACTUS. This proposed paper contained reviews of the above techniques by using a more powerful programming language (Python) as a tool. Evidence attainable from this study is helpful for researchers to select an appropriate clustering approach based on their domain.

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Design of Novel Metaheuristic Techniques for Clustering

Cited by 5 publications

References 16 publications

A Metaheuristic Technique for Cluster-Based Feature Selection of DNA Methylation Data for Cancer

A Metaheuristic Technique for Cluster-Based Feature Selection of DNA Methylation Data for Cancer

Automatic Clustering of DNA Sequences With Intelligent Techniques

Systematic Review: A State of Art ML Based Clustering Algorithms for Data Mining

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