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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 4/2018

22.02.2018

Research on Attribute Dimension Partition Based on SVM Classifying and MapReduce

verfasst von: Wenbin Zhao, Tongrang Fan, Yongchuan Nie, Feng Wu, Hou Wen

Erschienen in: Wireless Personal Communications | Ausgabe 4/2018

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Abstract

The data analysis is closely related to data attribute dimension. The traditional extraction and partition of data attribute dimension is so manual and inefficiency as to not meet the needs of analysing big data. This paper proposed an attribute dimension partition scheme based on SVM classifying and MapReduce for analysing big data. This scheme improve traditional SVM classifying method by combining Euclidean distance theory for overcoming its disadvantages, and adopts punish coefficient to reduce the unbalance of data distribution. With the improved SVM classifying method, the implementation of attribute dimension partition take MapReduce model of Hadoop as process engine, use TF–IDF vector to save the extracted attribute dimension, and use k-means clustering algorithm to clustering partition. The experiment result shows that the execution efficiency of the proposed method is enhanced, and while the rationality of partition is guaranteed, the increasing of data attributes does not significantly increase the execution time.
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Metadaten
Titel
Research on Attribute Dimension Partition Based on SVM Classifying and MapReduce
verfasst von
Wenbin Zhao
Tongrang Fan
Yongchuan Nie
Feng Wu
Hou Wen
Publikationsdatum
22.02.2018
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 4/2018
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-018-5301-9

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