A Grid and Density Based Fast Spatial Clustering Algorithm

Huang, Ming; Fuling, Bian

doi:10.1109/aici.2009.228

Cited by 16 publications

(9 citation statements)

References 1 publication

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“…We propose a hierarchical multilevel grid summarization approach to aggregate the data. Similar method has been used in spatial data mining [37] and robotic mapping [38]. In this method, each dimension in the dataset is divided into a limited number of equal width and nonoverlapping regions (intervals).…”

Section: Multi-resolution Tree Structure For Data Aggregationmentioning

confidence: 99%

Bayes Classification and Entropy Discretization of Large Datasets using Multi-Resolution Data Aggregation

Alwajidi¹,

Yang²

2020

Adv. sci. technol. eng. syst. j.

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Big data analysis has important applications in many areas such as sensor networks and connected healthcare. High volume and velocity of big data bring many challenges to data analysis. One possible solution is to summarize the data and provides a manageable data structure to hold a scalable summarization of data for efficient and effective analysis. This research extends our previous work on developing an effective technique to create, organize, access, and maintain summarization of big data and develops algorithms for Bayes classification and entropy discretization of large data sets using the multi-resolution data summarization structure. Bayes classification and data discretization play essential roles in many learning algorithms such as decision tree and nearest neighbor search. The proposed method can handle streaming data efficiently and, for entropy discretization, provide sufficient information to find the optimal split variable and the optimal split value.

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Section: Multi-resolution Tree Structure For Data Aggregationmentioning

confidence: 99%

Bayes Classification and Entropy Discretization of Large Datasets using Multi-Resolution Data Aggregation

Alwajidi¹,

Yang²

2020

Adv. sci. technol. eng. syst. j.

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“…Data clustering techniques are effective tools to discover high-relevancy segments for extracting and analyzing the traffic information. Several clustering methods were widely used in data mining, such as partition-based clustering (PBC) [29], [30], density-based clustering (DBC) [31], [32], grid-based clustering (GBC) [33], [34], and hierarchy-based clustering (HBC) [35], [36]. These methods cluster data into different groups in accordance with the similarities, the distance between the data and cluster centers or the density of each group.…”

Section: A: Network Clusteringmentioning

confidence: 99%

Utilizing Artificial Neural Network in GPS-Equipped Probe Vehicles Data- Based Travel Time Estimation

Guo

Fang

et al. 2019

IEEE Access

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Real-time traffic status information provides good references for urban traffic control and management. Travel time is easy to understand and widely employed in representing traffic status. With significantly improved positioning accuracy and coverage, trajectory data collected from GPS-equipped probe vehicles have great potential for traffic state recognition. This paper presents a machine learning enabled travel time estimation method based on the GPS-equipped probe vehicles data. This research considers the spatial-temporal relevancy while solving the travel time allocation problem: the travel time of target segment might be associated with its previous travel times and/or the traffic states of nearby relevant segments. After data normalization and network clustering, an artificial neural network (ANN) algorithm considering such spatial-temporal relevancy was conducted to infer the travel time distribution among the traveled segments within one path. Furthermore, a weighted summation of the travel time estimation result from various trajectories was calculated to better represent the segment travel time in one time step. The proposed method was evaluated by evaluating the estimation results with automatic vehicle identification obtained ground truth. The experimental results illustrated that by utilizing the ANN to consider the spatialtemporal relevancy, the proposed method is effective and efficient in estimating the travel time. INDEX TERMS Artificial neural network (ANN), data clustering, global positioning system (GPS) equipped probe vehicle, travel time estimation, travel time allocation.

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“…Continue this process until all points have been processed. Finally, no ID points as noise points [14,15].…”

Section: The Basic Concept Of Dbscanmentioning

confidence: 99%

G-DBSCAN: An Improved DBSCAN Clustering Method Based On Grid

Ma¹,

Li²,

Qiao³

et al. 2014

Advanced Science and Technology Letters

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Clustering is one of the most active research fields in data mining. Clustering in statistics, pattern recognition, image processing, machine learning, biology, marketing and many other fields have a wide range of applications. DBSCAN is a density-based clustering algorithm. this algorithm clusters data of high density. The traditional DBSCAN clustering algorithm in finding the core object, will use this object as the center core, extends outwards continuously. At this point, the core objects growing, unprocessed objects are retained in memory, which will occupy a lot of memory and I/O overhead, algorithm efficiency is not high. In order to ensure the high efficiency of DBSCAN clustering algorithm, and reduce its memory footprint. In this paper, the original DBSCAN algorithm was improved, and the G-DBSCAN algorithm is proposed. G-DBSCAN algorithm to reduce the number of query object as a starting point, Put the data into the grid, with the center point of the data in the grid to replace all the grid points as the algorithm input. The query object will be drastically reduced, thus improving the efficiency of the algorithm, reduces the memory footprint. The results prove that G-DBSCAN algorithm is feasible and effective.

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A Grid and Density Based Fast Spatial Clustering Algorithm

Cited by 16 publications

References 1 publication

Bayes Classification and Entropy Discretization of Large Datasets using Multi-Resolution Data Aggregation

Bayes Classification and Entropy Discretization of Large Datasets using Multi-Resolution Data Aggregation

Utilizing Artificial Neural Network in GPS-Equipped Probe Vehicles Data- Based Travel Time Estimation

G-DBSCAN: An Improved DBSCAN Clustering Method Based On Grid

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