CC_TRS: Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life

Musaab Riyadh,Md Nasir Sulaiman,Norwati Mustapha,Nurfadhlina Binti Mohd Sharef

doi:10.1155/2017/7523138

Musaab Riyadh, Md Nasir Sulaiman + Show 2 more

Open Access

https://doi.org/10.1155/2017/7523138

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Abstract

The rapid spreading of positioning devices leads to the generation of massive spatiotemporal trajectories data. In some scenarios, spatiotemporal data are received in stream manner. Clustering of stream data is beneficial for different applications such as traffic management and weather forecasting. In this article, an algorithm for Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life is proposed. The algorithm consists of two phases. There is the online phase where temporal micro clusters are used to store summarized spatiotemporal information for each group of similar segments. The clustering task in online phase is based on temporal micro cluster lifetime instead of time window technique which divides stream data into time bins and clusters each bin separately. For offline phase, a density based clustering approach is used to generate macro clusters depending on temporal micro clusters. The evaluation of the proposed algorithm on real data sets shows the efficiency and the effectiveness of the proposed algorithm and proved it is efficient alternative to time window technique.

Highlights

IntroductionMoving objects such as vehicles and animals are equipped with GPS devices; these devices leave digital traces (latitude, longitude) position at each moment
Moving objects such as vehicles and animals are equipped with GPS devices; these devices leave digital traces position at each moment
Starting clustering from scratch in each time bin leads to the following. (i) Disturbance occurs in clustering quality which centralizes in the border area between two adjacent time bins specially if it is very dense of trajectory segments since clustering process in time window technique creates new micro clusters (MCn) for some segments Si at the start of each timebink+1 even though these segments are very close to Mathematical Problems in Engineering

Summary

Introduction

Moving objects such as vehicles and animals are equipped with GPS devices; these devices leave digital traces (latitude, longitude) position at each moment. Many algorithms of data stream clustering depend on object based paradigm which consists of two phases: online phase and offline phase. Many existing algorithms such as TCMM and ConTraClu exploit time window technique to incrementally cluster trajectory data stream. (i) Disturbance occurs in clustering quality which centralizes in the border area between two adjacent time bins specially if it is very dense of trajectory segments since clustering process in time window technique creates new micro clusters (MCn) for some segments Si at the start of each timebink+1 even though these segments are very close (within distance threshold) to MC1 Starting clustering from scratch in each time bin leads to the following. (i) Disturbance occurs in clustering quality which centralizes in the border area between two adjacent time bins specially if it is very dense of trajectory segments since clustering process in time window technique creates new micro clusters (MCn) for some segments Si at the start of each timebink+1 even though these segments are very close (within distance threshold) to MC1

A Cluster life

Related Works

Problem Definition

Performance Evaluation

Findings

Conclusion