The behavior of particles in the Particle Swarm Clustering algorithm

Szabó, Adrienn; Prior, Ana Karina. F.; Castro, Leandro Nunes de

doi:10.1109/fuzzy.2010.5584118

Cited by 5 publications

(7 citation statements)

References 5 publications

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“…Clustering methods have been widely used in various applications, such as statistics, software engineering, biology, psychology and other social sciences, in order to group the similar objects/instances in large amounts of data [ 57 , 58 , 59 , 60 ]. In any pattern recognition applications, escalating the inter-class variance and diminishing the intra-class variance of the attributes or features are the fundamental issues to improve the classification/recognition accuracy [ 57 , 58 , 59 , 60 ]. High intra-class variance and low inter-class variance among the features may degrade the performance of classifiers which results in poor emotion recognition rates.…”

Section: Methodsmentioning

confidence: 99%

Particle Swarm Optimization Based Feature Enhancement and Feature Selection for Improved Emotion Recognition in Speech and Glottal Signals

2015

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In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature.

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Section: Methodsmentioning

confidence: 99%

Particle Swarm Optimization Based Feature Enhancement and Feature Selection for Improved Emotion Recognition in Speech and Glottal Signals

2015

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“…Di PSC, partikel yang akan diperbarui didefinisikan oleh input data (yaitu hanya pemenangnya -yang paling dekat dengan input yang dipertimbangkan -diperbarui sesuai dengan persamaan kecepatan dan posisi). Algoritma PSC menggunakan kecerdasan kolektif untuk memecahkan masalah dalam teknik clustering [4]. Adapun pseudocode dari PSC adalah sebagai berikut [20][21][22][23][24] : Algorithm S = PSC (dataset, max_iteration, vmax, nc, ) Inisialisasikan nc partikel, random x, dan inisialisasikan v ke nol Hitung jarak dari p, g, dari setiap partikel pada setiap datum…”

Section: B K-meansunclassified

Perbandingan Rapid Centroid Estimation (RCE) — K Nearest Neighbor (K-NN) Dengan K Means — K Nearest Neighbor (K-NN)

2017

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Teknik Clustering terbukti dapat meningkatkan akurasi dalam melakukan klasifikasi, terutama pada algoritma K-Nearest Neighbor (K-NN). Setiap data dari setiap kelas akan membentuk K cluster yang kemudian nilai centroid akhir dari setiap cluster pada setiap kelas data tersebut akan dijadikan data acuan untuk melakukan proses klasifikasi menggunakan algoritma K-NN. Namun kendala dari banyaknya teknik clustering adalah biaya komputasi yang mahal, Rapid Centroid Estimation (RCE) dan K-Means termasuk kedalam teknik clustering dengan biaya komputasi yang murah. Untuk melihat manakah dari kedua algoritma ini (RCE dan K-Means) yang lebih baik memberikan peningkatan akurasi pada algoritma K-NN maka, pada penelitian ini akan mencoba untuk membandingkan kedua algoritma tersebut. Hasil dari penelitian ini adalah gabungan RCE—K-NN memberikan hasil akurasi yang lebih baik dari K-Means—K-NN pada data set iris dan wine. Namun dalam perubahan nilai akurasi RCE—K-NN lebih stabil hanya pada data set iris. Sedangkan pada data set wine, K-Means—K-NN terlihat mendapati perubahan akurasi yang lebih stabil dibandingkan RCE—K-NN.

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“…This perturbation should appropriately contributed by the experience of the individual (individual cognition), and the influence of the social environment (social interaction) in which the individual is inserted (7). (8) mPSC contributes to the PSC algorithm by eliminating the need for inertia weight ω and velocity clamp. However, referring to the well known convergence problem of PSO [8], we argue that the memory-less nature of mPSC and the removal of inertia weight diminishes the exploration and exploitation shifts of the particles.…”

Section: B Modified Particle Swarm Clustering (Mpsc)mentioning

confidence: 99%

“…(8) mPSC contributes to the PSC algorithm by eliminating the need for inertia weight ω and velocity clamp. However, referring to the well known convergence problem of PSO [8], we argue that the memory-less nature of mPSC and the removal of inertia weight diminishes the exploration and exploitation shifts of the particles. This may lead to an unfavorable tendency to converge to local minima instead of the global minimum on more complex datasets.…”

Section: B Modified Particle Swarm Clustering (Mpsc)mentioning

confidence: 99%

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Fast unsupervised learning method for rapid estimation of cluster centroids

Yuwono

Moulton

et al. 2012

2012 IEEE Congress on Evolutionary Computation

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Data clustering is a process where a set of data points is divided into groups of similar points. Recent approaches for data clustering have seen the development of unsupervised learning algorithms based on Particle Swarm Optimization (PSO) techniques. These include Particle Swarm Clustering (PSC) and Modified PSC (mPSC) algorithms for solving clustering problems. However, the PSC and mPSC algorithms tend to be computationally expensive when applied to datasets that have higher levels of dimensionality and large volumes. This paper presents a novel and more efficient swarm clustering strategy we call Rapid Centroid Estimation (RCE). We compare the performance of RCE with the performance of PSC and mPSC in several ways including complexity analyses and particle behavior analyses. Our benchmark testing suggests that RCE can reach a solution 274 times quicker than PSC and 270 times quicker than mPSC for a clustering task where the dataset has a dimension of 80 and a volume of 500. We also investigated particle behaviors on two-class two-dimensional datasets with volume of 500, presenting 250 data for each well-separated class with known Gaussian centers. We found that RCE converged to the appropriate centers at 70 updates on average, compared to 19802 updates for PSC and 23006 updates for mPSC. An ANOVA indicates RCE is significantly faster than both PSC and mPSC.

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The behavior of particles in the Particle Swarm Clustering algorithm

Cited by 5 publications

References 5 publications

Particle Swarm Optimization Based Feature Enhancement and Feature Selection for Improved Emotion Recognition in Speech and Glottal Signals

Particle Swarm Optimization Based Feature Enhancement and Feature Selection for Improved Emotion Recognition in Speech and Glottal Signals

Perbandingan Rapid Centroid Estimation (RCE) — K Nearest Neighbor (K-NN) Dengan K Means — K Nearest Neighbor (K-NN)

Fast unsupervised learning method for rapid estimation of cluster centroids

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