An Automatic K-Means Clustering Algorithm of GPS Data Combining a Novel Niche Genetic Algorithm with Noise and Density

Zhou, Xiangbing; Gu, Jianggang; Shen, Shao-Peng; Ma, Hongjiang; Miao, Fang; Hua, Zhang; Gong, Honghan

doi:10.3390/ijgi6120392

Cited by 30 publications

(27 citation statements)

References 44 publications

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“…Zhou et al [17] Cluster analysis Real life and artificial datasets Fitness function evaluation Pacheco et al [104] Cluster analysis Real life datasets SI Elaziz et al [105] Cluster analysis Real life and artificial datasets Dunn index, SI, DB index and Calinski-Harabasz (CH) index Chowdhury and Das [37] Pattern recognition Real life and artificial datasets Huang's accuracy measure Sheng et al [106] Miscellaneous Real life and artificial datasets DB, CH, I-index Zhou et al [107] GPS data based trajectory Real life: Taxi GPS Datasets DB index Agbaje et al [108] Cluster analysis Real life datasets DB and CS indices problem at hand. From this analysis, GA has 887, PSO has 524, DE has 180, FA has 49, and DE has 9 published documents.…”

Section: Real Life Datasets S_dbw Indexmentioning

confidence: 99%

Nature-inspired metaheuristic techniques for automatic clustering: a survey and performance study

Ezugwu

2020

SN Appl. Sci.

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The application of several swarm intelligence and evolutionary metaheuristic algorithms in data clustering problems has in the past few decades gained wide popularity and acceptance due to their success in solving and finding good quality solutions to a variety of complex real-world optimization problems. Clustering is considered one of the most important data analysis techniques in the domain of data mining. A clustering problem refers to the partitioning of unlabeled data objects into a certain number of clusters based on their attribute values or features, with the objective of maximizing intra-clusters homogeneity and inter-cluster heterogeneity. This paper presents an up-to-date survey of major nature-inspired metaheuristic algorithms that have been employed to solve automatic clustering problems. Further, a comparative study of several modified well-known global metaheuristic algorithms is carried out to solve automatic clustering problems. Also, three hybrid swarm intelligence and evolutionary algorithms, namely, particle swarm differential evolution algorithm, firefly differential evolution algorithm and invasive weed optimization differential evolution algorithm, are proposed to deal with the task of automatic data clustering. In contrast to many of the existing traditional and evolutionary computational clustering techniques, the clustering algorithms presented in this paper do not require any predetermined information or prior-knowledge of the dataset that is to be classified, but rather they are capable of spontaneously identifying the optimal number of partitions of the data points during the course of program execution. Forty-one benchmarked datasets that comprise eleven artificial and thirty real world datasets are collated and utilized to evaluate the performances of the representative nature-inspired clustering algorithms. According to the extensive experimental results, comparisons and statistical significance, the firefly algorithm appeared to be more appropriate for better clustering of both low and high dimensional data objects than were other state-of-the-art algorithms. Further, an experimental study demonstrates the superiority of the three proposed hybrid algorithms over the standard stateof-the-art methods in finding meaningful clustering solutions to the problem at hand.

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Section: Real Life Datasets S_dbw Indexmentioning

confidence: 99%

Nature-inspired metaheuristic techniques for automatic clustering: a survey and performance study

Ezugwu

2020

SN Appl. Sci.

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“…The sampling frequency was controlled in two minutes (≤2 min); namely, if different sample rates were less than or equal to two minutes, then different location information was recorded, which consisted of the GPS data points of the approximately 30 thousand taxis in 8:50-8:59 a.m. on 20 March 2016. When the origins and destinations (OD) were extracted and mined using a clustering algorithm in Reference [30], this dataset only contained 71,375 OD points in total, as shown in Figure 2. In particular, the OD points are usually used to describe trajectory patterns [8,31].…”

Section: Description Of Real-world Taxi Gps Datamentioning

confidence: 99%

A Trajectory Regression Clustering Technique Combining a Novel Fuzzy C-Means Clustering Algorithm with the Least Squares Method

Zhou

Miao

et al. 2018

IJGI

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Rapidly growing GPS (Global Positioning System) trajectories hide much valuable information, such as city road planning, urban travel demand, and population migration. In order to mine the hidden information and to capture better clustering results, a trajectory regression clustering method (an unsupervised trajectory clustering method) is proposed to reduce local information loss of the trajectory and to avoid getting stuck in the local optimum. Using this method, we first define our new concept of trajectory clustering and construct a novel partitioning (angle-based partitioning) method of line segments; second, the Lagrange-based method and Hausdorff-based K-means++ are integrated in fuzzy C-means (FCM) clustering, which are used to maintain the stability and the robustness of the clustering process; finally, least squares regression model is employed to achieve regression clustering of the trajectory. In our experiment, the performance and effectiveness of our method is validated against real-world taxi GPS data. When comparing our clustering algorithm with the partition-based clustering algorithms (K-means, K-median, and FCM), our experimental results demonstrate that the presented method is more effective and generates a more reasonable trajectory.

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“…A disadvantage of K-Means is that it is easy to fall into local optima. As a remedy, a popular trend is to integrate the genetic algorithm [7,8] with K-means to obtain genetic K-means algorithms [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. K-Means is also combined with fuzzy mechanism to obtain fuzzy C-Means [24,25].…”

Section: Introductionmentioning

confidence: 99%

A Genetic XK-Means Algorithm with Empty Cluster Reassignment

Hua

Zhang

et al. 2019

Symmetry

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K-Means is a well known and widely used classical clustering algorithm. It is easy to fall into local optimum and it is sensitive to the initial choice of cluster centers. XK-Means (eXploratory K-Means) has been introduced in the literature by adding an exploratory disturbance onto the vector of cluster centers, so as to jump out of the local optimum and reduce the sensitivity to the initial centers. However, empty clusters may appear during the iteration of XK-Means, causing damage to the efficiency of the algorithm. The aim of this paper is to introduce an empty-cluster-reassignment technique and use it to modify XK-Means, resulting in an EXK-Means clustering algorithm. Furthermore, we combine the EXK-Means with genetic mechanism to form a genetic XK-Means algorithm with empty-cluster-reassignment, referred to as GEXK-Means clustering algorithm. The convergence of GEXK-Means to the global optimum is theoretically proved. Numerical experiments on a few real world clustering problems are carried out, showing the advantage of EXK-Means over XK-Means, and the advantage of GEXK-Means over EXK-Means, XK-Means, K-Means and GXK-Means (genetic XK-Means).

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An Automatic K-Means Clustering Algorithm of GPS Data Combining a Novel Niche Genetic Algorithm with Noise and Density

Cited by 30 publications

References 44 publications

Nature-inspired metaheuristic techniques for automatic clustering: a survey and performance study

Nature-inspired metaheuristic techniques for automatic clustering: a survey and performance study

A Trajectory Regression Clustering Technique Combining a Novel Fuzzy C-Means Clustering Algorithm with the Least Squares Method

A Genetic XK-Means Algorithm with Empty Cluster Reassignment

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