Optimal Clustering Method Based on Genetic Algorithm

Gajawada, Satish; Toshniwal, Durga; Patil, Nagamma; Garg, Kumkum

doi:10.1007/978-81-322-0491-6_29

Cited by 12 publications

(5 citation statements)

References 14 publications

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“…To that end, Gajawada et al [16] proposed an optimal clustering genetic algorithm (OCGA) with which to identify the optimal number of clusters. The OCGA managed to determine a suitable number of clusters using a relatively smaller number of iterations than previous algorithms.…”

Section: Related Work 31 Clusteringmentioning

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: Related Work 31 Clusteringmentioning

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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“…In 2012, Satish in [29] proposed a genetic algorithm called (OCGA) that estimates the number of clusters. Firstly, hierarchical clustering is applied to the data set to obtain a dendrogram where each level represents a different number of clusters and different clusters formation.…”

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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“…It may be noted that, here, the objective was not to identify the optimum y and x values, rather the frequency at which the cost was minimum. This procedure was therefore, similar to the implementation of internal cluster validation optimization methods (Bezdek, Pal, 1995;Gajawada et al, 2012) used in machine learning. The reportedly robust Nelder-Mead optimization procedure (Singer, Nelder, 2009) was implemented using the optim function in R (R, core team, 2013).…”

Section: Entropy-based Frequency Optimizationmentioning

confidence: 99%

Multi-Scale Time Series Analysis of Evapotranspiration for High-Throughput Phenotyping Frequency Optimization

Kar

Tanaka

Iwata

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. This work is undertaken considering the significance of functional phenotyping (primarily measured from continuous profiles of plant-water relations) for crop selection purposes. High-Throughput Plant Phenotyping (HTPP) platforms which largely employ state-of-the-art sensor technologies for acquisition of vast amount of field data, often fail to efficiently translate sensor information into knowledge due to the major challenges of data handling and processing. Hence, it is imperative to concurrently find a way for dissociating noise from useful data. Additionally, another important aspect is understanding how frequent should be the data collection, so that information is maximized. This paper presents a novel approach for identifying the optimal frequency for phenotyping evapotranspiration (ET) by assimilating results from both time series forecast as well as classification models. Thus, at the optimal frequency, plant-water relations can not only be desirably predicted but genotypes can also be classified based on the characteristics of their ET profiles. Consequently, this will aid better crop selection, besides minimizing noise, redundancy, cost and effort in HTPP data collection. High frequency (15 min) ET time series data of 48 chickpea varieties (with considerable genotypic diversity) collected at the LeasyScan HTPP platform, ICRISAT is used for this study. Time series forecast and classification is performed by varying frequency up to 180 min. Multiple performance measures of time series forecast and classification are combined, followed by implementation of entropy theory for sampling frequency optimization. The results demonstrate that ET time series with a frequency of 60 min per day potentially yield the optimum information.

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Optimal Clustering Method Based on Genetic Algorithm

Cited by 12 publications

References 14 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

Multi-Scale Time Series Analysis of Evapotranspiration for High-Throughput Phenotyping Frequency Optimization

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