A scalable and fast OPTICS for clustering trajectory big data

Deng, Ze; Hu, Yangyang; Zhu, Mao; Huang, Xiaohui; Du, Bo

doi:10.1007/s10586-014-0413-9

Cited by 67 publications

(36 citation statements)

References 37 publications

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“…However, unlike Yuan et al (2011) this approach does not use a scoring metric, but rather a geometric distance function between trajectory line segments. Lastly, Deng, Hu, Zhu, Huang, and Du (2014) introduce a parallelised GPGPU approach that uses a spatio-temporal index and distance function to perform density based trajectory clustering. Therefore, we highlight again that, of the literature presented none can perform trajectory clustering using any arbitrary number of dimensions.…”

Section: Fixed Dimensionality Index Methodsmentioning

confidence: 99%

A general methodology for n-dimensional trajectory clustering

Bermingham

Lee

2015

Expert Systems with Applications

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Section: Fixed Dimensionality Index Methodsmentioning

confidence: 99%

A general methodology for n-dimensional trajectory clustering

Bermingham

Lee

2015

Expert Systems with Applications

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“…Yet another challenge is to minimize the iterations of data processing, which are typically required in clustering algorithms. Interestingly, there have been some recent efforts towards mining mobility data in a distributed way, such as mining co-movement patterns [5], identifying frequent patterns [21] or adapting already existing distributed solutions to trajectory data [3], yet no approach for distributed subtrajectory clustering exists as of now.…”

Section: Introductionmentioning

confidence: 99%

Scalable Distributed Subtrajectory Clustering

Tampakis¹,

Pelekis²,

Doulkeridis³

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

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Trajectory clustering is an important operation of knowledge discovery from mobility data. Especially nowadays, the need for performing advanced analytic operations over massively produced data, such as mobility traces, in efficient and scalable ways is imperative. However, discovering clusters of complete trajectories can overlook significant patterns that exist only for a small portion of their lifespan. In this paper, we address the problem of Distributed Subtrajectory Clustering in an efficient and highly scalable way. The problem is challenging because the subtrajectories to be clustered are not known in advance, but they need to be discovered dynamically based on adjacent subtrajectories in space and time. Towards this objective, we split the original problem to three sub-problems, namely Subtrajectory Join, Trajectory Segmentation and Clustering and Outlier Detection, and deal with each one in a distributed fashion by utilizing the MapReduce programming model. The efficiency and the effectiveness of our solution is demonstrated experimentally over a synthetic and two large real datasets from the maritime and urban domains and through comparison with two state of the art subtrajectory clustering algorithms.

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“…Multi-sensor fusion has always been concerned for complementary information enhancement [1], especially for the remote sensing big data era [2][3][4][5]. With increasing frequency, different types of remote sensing satellites are used to monitor the Earth's surface.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Fusion of Remote Sensing Images with Structural Sparsity and Semi-Coupled Dictionary Learning

et al. 2016

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Fusion of remote sensing images with different spatial and temporal resolutions is highly needed by diverse earth observation applications. A small number of spatiotemporal fusion methods using sparse representation appear to be more promising than traditional linear mixture methods in reflecting abruptly changing terrestrial content. However, one of the main difficulties is that the results of sparse representation have reduced expressional accuracy; this is due in part to insufficient prior knowledge. For remote sensing images, the cluster and joint structural sparsity of the sparse coefficients could be employed as a priori knowledge. In this paper, a new optimization model is constructed with the semi-coupled dictionary learning and structural sparsity to predict the unknown high-resolution image from known images. Specifically, the intra-block correlation and cluster-structured sparsity are considered for single-channel reconstruction, and the inter-band similarity of joint-structured sparsity is considered for multichannel reconstruction, and both are implemented with block sparse Bayesian learning. The detailed optimization steps are given iteratively. In the experimental procedure, the red, green, and near-infrared bands of Landsat-7 and Moderate Resolution Imaging Spectrometer (MODIS) satellites are put to fusion with root mean square errors to check the prediction accuracy. It can be concluded from the experiment that the proposed methods can produce higher quality than state-of-the-art methods.

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A scalable and fast OPTICS for clustering trajectory big data

Cited by 67 publications

References 37 publications

A general methodology for n-dimensional trajectory clustering

A general methodology for n-dimensional trajectory clustering

Scalable Distributed Subtrajectory Clustering

Spatiotemporal Fusion of Remote Sensing Images with Structural Sparsity and Semi-Coupled Dictionary Learning

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