Particle swarm classification: A survey and positioning

Nouaouria, Nabila; Boukadoum, Mounir; Proulx, Robert

doi:10.1016/j.patcog.2012.12.011

Cited by 50 publications

(16 citation statements)

References 36 publications

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“…It contains 178 instances measured with 13 continuous features. The Abalone dataset [34,35] contains physical measurements of abalone shellfish. It contains 4177 instances with 9 features each (1 cluster label and 8 numeric and we apply 8 primary features), which are divided into 29 clusters.…”

Section: Resultsmentioning

confidence: 99%

“…The evaluation of clustering results is often referred to as cluster validation, and researchers have proposed many measures of cluster validity. In this paper, we choose six standard validity measures to examine the soundness of the clustering algorithms, including Davies-Bouldins index (DBI) [10,35,36], the Dunn validity index (DVI) [36,37], normalized mutual information (NMI) [38][39][40], the clustering cost function (ϕ), the Silhouette index (SI) [41,42], and the SD index (SDI) [42]. These measures are described as follows:…”

Section: Evaluation Metricsmentioning

confidence: 99%

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Self‐Adaptive K‐Means Based on a Covering Algorithm

et al. 2018

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The K-means algorithm is one of the ten classic algorithms in the area of data mining and has been studied by researchers in numerous fields for a long time. However, the value of the clustering number k in the K-means algorithm is not always easy to be determined, and the selection of the initial centers is vulnerable to outliers. This paper proposes an improved K-means clustering algorithm called the covering K-means algorithm (C-K-means). The C-K-means algorithm can not only acquire efficient and accurate clustering results but also self-adaptively provide a reasonable numbers of clusters based on the data features. It includes two phases: the initialization of the covering algorithm (CA) and the Lloyd iteration of the K-means. The first phase executes the CA. CA self-organizes and recognizes the number of clusters k based on the similarities in the data, and it requires neither the number of clusters to be prespecified nor the initial centers to be manually selected. Therefore, it has a “blind” feature, that is, k is not preselected. The second phase performs the Lloyd iteration based on the results of the first phase. The C-K-means algorithm combines the advantages of CA and K-means. Experiments are carried out on the Spark platform, and the results verify the good scalability of the C-K-means algorithm. This algorithm can effectively solve the problem of large-scale data clustering. Extensive experiments on real data sets show that the accuracy and efficiency of the C-K-means algorithm outperforms the existing algorithms under both sequential and parallel conditions.

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Section: Resultsmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

Self‐Adaptive K‐Means Based on a Covering Algorithm

et al. 2018

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“…The actual optimization in each subtask is carried out by a basic solver. In our case, we use Particle Swarm Optimization (PSO) [52] as it provides a straightforward way to adapt the optimizer to the reliability of initial solutions by controlling the overall number and starting points of the particles.…”

Section: Sequential Global Optimizationmentioning

confidence: 99%

Feature-based groupwise registration of historical aerial images to present-day ortho-photo maps

Zambanini

2019

Pattern Recognition

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In this paper, we address the registration of historical WWII images to presentday ortho-photo maps for the purpose of geolocalization. Due to the challenging nature of this problem, we propose to register the images jointly as a group rather than in a step-by-step manner. To this end, we exploit Hough Voting spaces as pairwise registration estimators and show how they can be integrated into a probabilistic groupwise registration framework that can be efficiently optimized. The feature-based nature of our registration framework allows to register images with a-priori unknown translational and rotational relations, and is also able to handle scale changes of up to 30% in our test data due to a final geometrically guided matching step. The superiority of the proposed method over existing pairwise and groupwise registration methods is demonstrated on eight highly challenging sets of historical images with corresponding ortho-photo maps.

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“…The 'learned' neural function enabled us to achieve two aims: it was used as a predictive tool, estimating germination rate values at any temperature; and it functioned as an objective function (meta-heuristic) of an optimization algorithm that was capable of providing the neural function with near-optimum solutions, after which the Particle Swarm Optimization (PSO, see [36] and [23] for details about this neural model) was used to obtain the near-optimum germination rate value for each cultivar.…”

Section: Modeling Procedures For Analyzing Germination Ratementioning

confidence: 99%

Applying computational intelligence methods to modeling and predicting common bean germination rates

Bianconi¹,

Watts

Huang

et al. 2014

2014 International Joint Conference on Neural Networks (IJCNN)

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The relationship between seed germination rate and environmental temperature is complex. This study assessed the effectiveness of multi-layer perceptron (MLP) and Particle Swarm Optimization (PSO) techniques in modeling and predicting the germination rate of two common bean cultivars as a function of distinct temperatures. MLP was utilized to model the germination rate of the cultivars and PSO was employed to determine the optimum temperatures at which the beans germinate most rapidly. The outcomes derived from implementing the MLP were compared with those obtained by means of a traditional statistical method. The MLP provided more accurate results than the conventional statistical regression in predicting germination rate values regarding the two common bean cultivars. The optimum germination rate values derived from implementing the PSO model were more accurate than those obtained by using the conventional quadratic regression.

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Particle swarm classification: A survey and positioning

Cited by 50 publications

References 36 publications

Self‐Adaptive K‐Means Based on a Covering Algorithm

Self‐Adaptive K‐Means Based on a Covering Algorithm

Feature-based groupwise registration of historical aerial images to present-day ortho-photo maps

Applying computational intelligence methods to modeling and predicting common bean germination rates

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