Automated clustering of trajectory data using a particle swarm optimization

Izakian, Zahedeh; Mesgari, Mehran; Abraham, Ajith

doi:10.1016/j.compenvurbsys.2015.10.009

Cited by 50 publications

(24 citation statements)

References 15 publications

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“…The degree of proneness is required to be defined and the dataset is required to undergo transformation from continuous dataset into categorical dataset. For , this process many researchers are using unsupervised methods such Kmeans [22], c-fuzzy clustering [23] methods and some are following -if then else‖ rules to segregate the dataset . In the field of supervised learning models, linear classifiers [14] such as perceptron learning models , Linear discriminant model, logistic regression [24] , lasso regression [25] [26] and probability models such as Naïve Bayes [27] are widely used in medical classification tasks .…”

Section: Reviewmentioning

confidence: 99%

Supervised Linear Estimator Modeling (SLEMH) for Health Monitoring

Kaur*,

Gupta

2019

IJEAT

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In this research work, the E-Health monitoring system has been developed using fifteen health indicators. These fifteen features were selected by following a Recursive Feature Elimination with Cross-Validation method. The dataset was labeled as per medical limits and segregated into three classes (normal, borderline and onset of unhealthy state). A rigorous process was followed at each step to find out which linear estimator and model is suitable for classifying health condition of persons. Five regression estimators were evaluated and it was found that logistic regression and linear discriminant analysis methods are providing highest accuracy and lowest error for classifying three health states of a patient.

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Section: Reviewmentioning

confidence: 99%

Supervised Linear Estimator Modeling (SLEMH) for Health Monitoring

Kaur*,

Gupta

2019

IJEAT

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“…There are two primary objects of clustering, discrete element and probability density function (PDF). Over the past few years, discrete element is preferred in clustering with a lot of works such as [1,2]. However, with the explosion of digital era, a massive amount of data is created each day [3,4], and how to present such data well is a challenge task for discrete elements.…”

Section: Introductionmentioning

confidence: 99%

A New Binary Adaptive Elitist Differential Evolution Based Automatic k‐Medoids Clustering for Probability Density Functions

Phamtoan

Vovan

Pham-Chau

et al. 2019

Mathematical Problems in Engineering

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This paper proposes an evolutionary computing based automatic partitioned clustering of probability density function, the so-called binary adaptive elitist differential evolution for clustering of probability density functions (baeDE-CDFs). Herein, the k-medoids based representative probability density functions (PDFs) are preferred to the k-means one for their capability of avoiding outlier effectively. Moreover, addressing clustering problem in favor of an evolutionary optimization one permits determining number of clusters “on the run”. Notably, the application of adaptive elitist differential evolution (aeDE) algorithm with binary chromosome representation not only decreases the computational burden remarkably, but also increases the quality of solution significantly. Multiple numerical examples are designed and examined to verify the proposed algorithm’s performance, and the numerical results are evaluated using numerous criteria to give a comprehensive conclusion. After some comparisons with other algorithms in the literature, it is worth noticing that the proposed algorithm reveals an outstanding performance in both quality of solution and computational time in a statistically significant way.

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“…Ahmadyfard and Modares by mixing K-means and PSO, and Lu et al by integrating K-means and genetic algorithm developed new methods for clustering, and their proposed methods proved superiority to K-means (Ahmadyfard and Modares, 2008;Lu et al, 2004). Also, Izakian et al, presented an automatic approach for trajectory clustering using PSO (Izakian et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Efficiency of the Optimization Approach for Clustering of Trajectories

Moayedi

Abbaspour

Chehreghan

2019

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

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Clustering is an unsupervised learning method that used to discover hidden patterns in large sets of data. Huge data volume and the multidimensionality of trajectories have made their clustering a more challenging task. K-means is a widely used clustering algorithm applied in the trajectory computation field. However, the critical issue with this algorithm is its dependency on the initial values and getting stuck in the local minimum. Meta-heuristic algorithms with the goal of minimizing the cost function of the K-means algorithm can be utilized to address this problem. In this paper, after suggesting a cost function, we compare clustering performance of seven known metaheuristic population-based algorithms including, Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), and Whale Optimization Algorithm (WOA). The results obtained from the clustering of several data sets with class labels were assessed by internal and external clustering validation indices along with computation time factor. According to the results, PSO, and SCA algorithms show the best results in the clustering regarding the Purity, and computation time metrics, respectively.

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Automated clustering of trajectory data using a particle swarm optimization

Cited by 50 publications

References 15 publications

Supervised Linear Estimator Modeling (SLEMH) for Health Monitoring

Supervised Linear Estimator Modeling (SLEMH) for Health Monitoring

A New Binary Adaptive Elitist Differential Evolution Based Automatic k‐Medoids Clustering for Probability Density Functions

A Comparison of Efficiency of the Optimization Approach for Clustering of Trajectories

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