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Feature Point Detection and Target Tracking Based on SIFT and KLT Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Research on a New Density Clustering Algorithm Based on MapReduce

Authors : Yun Wu, Zhixiong Zhang

Published in: Geo-Spatial Knowledge and Intelligence

Publisher: Springer Singapore

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Abstract

The empirical solution parameters for the Density-Based Spatial Clustering of Applications with Noise(DBSCAN) resulted in poor Clustering effect and low execution efficiency, An adaptive DBSCAN algorithm based on genetic algorithm and MapReduce programming framework is proposed. The genetic algorithm (minPts) and scanning radius size (Eps) optimized intensive interval threshold, at the same time, combined with the similarities and differences of data sets using the Hadoop cluster parallel computing ability of two specifications, the data is reasonable of serialization, finally realizes the adaptive parallel clustering efficiently. Experimental results show that the improved algorithm (GA) - DBSCANMR when dealing with the data set of magnitude 3 times execution efficiency is improved DBSCAN algorithm, clustering quality improved by 10%, and this trend increases as the amount of data, provides a more precise threshold DBSCAN algorithm to determine the implementation of the method.

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Metadata
Title
Research on a New Density Clustering Algorithm Based on MapReduce
Authors
Yun Wu
Zhixiong Zhang
Copyright Year
2018
Publisher
Springer Singapore
Book
Geo-Spatial Knowledge and Intelligence

Print ISBN: 978-981-13-0895-6

Electronic ISBN: 978-981-13-0896-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-13-0896-3_55

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