An incremental algorithm for clustering spatial data streams: exploring temporal locality

Ling, Wei; Peng, Wen-Chih

doi:10.1007/s10115-013-0636-8

Cited by 10 publications

(8 citation statements)

References 31 publications

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“…Clustering accuracy (ACC) discovers the one-to-one relationships between clusters and classes and measures the extent to which each cluster contains data points from the corresponding class. It is defined as [18,19]…”

Section: Baseline Algorithms and Evaluation Methodsmentioning

confidence: 99%

“…The number of nodes and number of clusters in each time step are given. The synthetic datasets are generated by two steps [18]. The first step generates the objects' geographic information, and the second step generates the values of the objects' nongeographic attributes.…”

Section: Synthetic Data Setsmentioning

confidence: 99%

“…To explore the spatial correlation between data sampled by different sensors for sensor networks, -local spatial clustering algorithm was proposed [17]. To explore the temporal locality features of sensor data, Wei and Peng [18] proposed an incremental clustering algorithm that clusters objects by inducing clustering results from previous time windows.…”

Section: Introductionmentioning

confidence: 99%

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Nonnegative Matrix Factorization-Based Spatial-Temporal Clustering for Multiple Sensor Data Streams

Sun

Sang

2014

Journal of Sensors

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Cyber physical systems have grown exponentially and have been attracting a lot of attention over the last few years. To retrieve and mine the useful information from massive amounts of sensor data streams with spatial, temporal, and other multidimensional information has become an active research area. Moreover, recent research has shown that clusters of streams change with a comprehensive spatial-temporal viewpoint in real applications. In this paper, we propose a spatial-temporal clustering algorithm (STClu) based on nonnegative matrix trifactorization by utilizing time-series observational data streams and geospatial relationship for clustering multiple sensor data streams. Instead of directly clustering multiple data streams periodically, STClu incorporates the spatial relationship between two sensors in proximity and integrates the historical information into consideration. Furthermore, we develop an iterative updating optimization algorithm STClu. The effectiveness and efficiency of the algorithm STClu are both demonstrated in experiments on real and synthetic data sets. The results show that the proposed STClu algorithm outperforms existing methods for clustering sensor data streams.

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Section: Baseline Algorithms and Evaluation Methodsmentioning

confidence: 99%

Section: Synthetic Data Setsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nonnegative Matrix Factorization-Based Spatial-Temporal Clustering for Multiple Sensor Data Streams

Sun

Sang

2014

Journal of Sensors

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“…FlowScan proposed by Li et al in [22] finds the hot routes in a road network by clustering the road segments based on the density of commonly shared traffic. The authors in [34] proposed an efficient incremental algorithm to cluster the spatial data streams collected from sensors. Their method first predicts the clusters roughly using the previous clustering results, and then refines them further in the next stage.…”

Section: Spatial Clustering and Spatial Network Partitioningmentioning

confidence: 99%

Partitioning road networks using density peak graphs: Efficiency vs. accuracy

Anwar

Liu

2017

Information Systems

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“…It heavily reduces the computations, while may sacrifice the quality of partitions marginally. There exist works on the complete partitioning of road networks [4,2], and some other related problems, including spatiotemporal propagation of congestion [5], identification of important road segments having high influence in propagating congestion [1], and incremental clustering of spatial data streams collected from sensors [7]. [3] identifies the congested partitions, and performs an experimental analysis by updating the partitions simply based on similarity in the traffic.…”

Section: Introductionmentioning

confidence: 99%

Tracking the Evolution of Congestion in Dynamic Urban Road Networks

Anwar

Liu

et al. 2016

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

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The congestion scenario on a road network is often represented by a set of differently congested partitions having homogeneous level of congestion inside. Due to the changing traffic, these partitions evolve with time. In this paper, we propose a two-layer method to incrementally update the differently congested partitions from those at the previous time point in an efficient manner, and thus track their evolution. The physical layer performs low-level computations to incrementally update a set of small-sized road network building blocks, and the logical layer provides an interface to query the physical layer about the congested partitions. At each time point, the unstable road segments are identified and moved to their most suitable building blocks. Our experimental results on different datasets show that the proposed method is much efficient than the existing re-partitioning methods without significant sacrifice in accuracy.

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An incremental algorithm for clustering spatial data streams: exploring temporal locality

Cited by 10 publications

References 31 publications

Nonnegative Matrix Factorization-Based Spatial-Temporal Clustering for Multiple Sensor Data Streams

Nonnegative Matrix Factorization-Based Spatial-Temporal Clustering for Multiple Sensor Data Streams

Partitioning road networks using density peak graphs: Efficiency vs. accuracy

Tracking the Evolution of Congestion in Dynamic Urban Road Networks

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