A Novel Clustering Algorithm Based on DPC and PSO

Cai, Jianghui; Wei, Huiling; Yang, Haifeng; Zhao, Xuehua

doi:10.1109/access.2020.2992903

Cited by 58 publications

(33 citation statements)

References 39 publications

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“…problems [30]. In addition for it being an "anytime algorithm" that produces solutions for any given computational time [31], its simple yet effective principle and solid global search capacity, leading to finding the optimal solution in relatively few iterations [28] were among the main motivations behind the choice of the algorithm as the main constituent of our approach. This section provides a description of the background algorithms behind the approach as well as the statistical preliminaries.…”

Section: Methodsmentioning

confidence: 99%

SDPSO: Spark Distributed PSO-based approach for feature selection and cancer disease prognosis

et al. 2021

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The Dimensionality Curse is one of the most critical issues that are hindering faster evolution in several fields broadly, and in bioinformatics distinctively. To counter this curse, a conglomerate solution is needed. Among the renowned techniques that proved efficacy, the scaling-based dimensionality reduction techniques are the most prevalent. To insure improved performance and productivity, horizontal scaling functions are combined with Particle Swarm Optimization (PSO) based computational techniques. Optimization algorithms are an interesting substitute to traditional feature selection methods that are both efficient and relatively easier to scale. Particle Swarm Optimization (PSO) is an iterative search algorithm that has proved to achieve excellent results for feature selection problems. In this paper, a composite Spark Distributed approach to feature selection that combines an integrative feature selection algorithm using Binary Particle Swarm Optimization (BPSO) with Particle Swarm Optimization (PSO) algorithm for cancer prognosis is proposed; hence Spark Distributed Particle Swarm Optimization (SDPSO) approach. The effectiveness of the proposed approach is demonstrated using five benchmark genomic datasets as well as a comparative study with four state of the art methods. Compared with the four methods, the proposed approach yields the best in average of purity ranging from 0.78 to 0.97 and F-measure ranging from 0.75 to 0.96.

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Section: Methodsmentioning

confidence: 99%

SDPSO: Spark Distributed PSO-based approach for feature selection and cancer disease prognosis

et al. 2021

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“…The problem of such method was more energy and time needed. Cai (2020) [8] was found new clustering approach depends on DPC to solve effect of selection parameter in the calculating the density and results of clustering. The drawback was complicated to assess the cut-off distance values depend on basis for selection.…”

Section: Related Workmentioning

confidence: 99%

An Energy Effectual Swarm Based Hierarchical Density Peak Clustering (HDPCS) Method for Multi-sensor Data Fusion Strategy

Brumancia¹,

Samuel²

2021

IJIES

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Clustering is the most frequently used physical architecture in data fusion, causing a problem as it has random distribution within local clusters, we propose a density peak based clustering algorithm utilizing a hierarchical approach. As a proficient method to joint decision-making of multi sensors, data fusion contains several benefits in data mining. In this work, we use the Salp Swarm Optimizer (SSO) algorithm based Hierarchical Density Peak Clustering (DPC) method to cluster the multiple sensor nodes as per the data similitude for the process of data fusion. Here, the higher density, distance and the cut off values are determined by using the SSO algorithm and then the chosen optimal fixed values were used to further process. Hierarchical Density Peak Clustering estimates the density and distance of each point. It is validated experimentally and compared it with the datasets and the error prediction with the test case numbers. The experimental results of the method are 96% accuracy determined from different datasets.

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“…It is a special reflecting Schmidt telescope with 4000 fibres and can be used to obtain spectra of celestial objects as well as sky background and calibration sources. The spectra used in this study are from the fifth data release of LAMOST (LAMOST DR5) [30], [31]. Up until July 2017, LAMOST had completed its first five years of regular surveys that began in September 2012.…”

Section: Datamentioning

confidence: 99%

Spectra-GANs: A New Automated Denoising Method for Low-S/N Stellar Spectra

Pan

et al. 2020

IEEE Access

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Numerous spectra can be obtained from sky surveys such as the Sloan Digital Sky Survey and the Large Sky Area Multi-Object Fibre Spectroscopic Telescope. However, a considerable fraction of such spectra, which are also valuable for astronomical research, are of low quality, possessing characteristics such as low signal-to-noise ratio (low-S/N). Principal component analysis is widely used to process these low-S/N spectra, but it is not efficient enough to describe the non-linear properties within the spectra. Wavelets are often used to denoise the low-S/N spectra. However, as is well known, the most optimal wavelet basis for each type of spectra needs to be determined; therefore, wavelet analysis is very difficult to use in practice. Restricted Boltzmann machine is a non-linear algorithm that performs poorly when applied to low-S/N spectra. Denoising Convolutional Neural Networks (DnCNN) is a promising denoiser, however, its performance is unsatisfactory due to the lack of suitable noise model. To better exploit the spectra with low-S/N, we propose a new method that can be used to obtain better denoised spectra when compared to those obtained using other methods. A new method called the Spectra Generative Adversarial Nets (Spectra-GANs) is introduced. Spectra-GANs is simply a feedforward neural network that learns the difference between the input vector and the target by minimizing the loss function. It can be used in spectral denoising. The performance of Spectra-GANs is better than those of other methods with regard to denoising the spectra, especially with regard to extremely low-S/N spectral processing. Thus, Spectra-GANs proposed herein is a suitable alternative to previously used methods in spectral denoising.

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A Novel Clustering Algorithm Based on DPC and PSO

Cited by 58 publications

References 39 publications

SDPSO: Spark Distributed PSO-based approach for feature selection and cancer disease prognosis

SDPSO: Spark Distributed PSO-based approach for feature selection and cancer disease prognosis

An Energy Effectual Swarm Based Hierarchical Density Peak Clustering (HDPCS) Method for Multi-sensor Data Fusion Strategy

Spectra-GANs: A New Automated Denoising Method for Low-S/N Stellar Spectra

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