Multidimensional Discrete Big Data Clustering Algorithm Based on Dynamic Grid

Traditionally, the data clustering algorithm is lack of comprehensive performance, leading to low clustering purity and long clustering time. In addition, the consistency between the clustering results and the original data distribution is not strong. Therefore, the multidimensional discrete big dat...

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Published in:	Wireless communications and mobile computing Vol. 2022; no. 1
Main Author:	Li, Xiaolei
Format:	Journal Article
Language:	English
Published:	Oxford Hindawi 2022 John Wiley & Sons, Inc
Subjects:	Algorithms Big Data Cluster analysis Clustering Communication Data mining Datasets Information technology Methods Principal components analysis Purity
ISSN:	1530-8669, 1530-8677
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Abstract	Traditionally, the data clustering algorithm is lack of comprehensive performance, leading to low clustering purity and long clustering time. In addition, the consistency between the clustering results and the original data distribution is not strong. Therefore, the multidimensional discrete big data clustering algorithm based on dynamic grid was put forward. Firstly, multidimensional discrete big data was processed in advance. The principal component analysis was used to reduce the dimension of data. The concept of entropy was introduced to divide the key attributes and noncritical attributes, so as to extract the key attributes. According to the results of data preprocessing, the dynamic grid was partitioned. According to the results, OptiGrid in data clustering algorithm was used to achieve the data clustering. The experimental results show that the clustering purity of this algorithm is between 95% and 100%, which is significantly higher than the traditional algorithm. Therefore, the multidimensional discrete big data clustering algorithm based on dynamic grid has better comprehensive performance, closer clustering shape to the original data distribution, higher clustering purity, and faster execution efficiency.
AbstractList	Traditionally, the data clustering algorithm is lack of comprehensive performance, leading to low clustering purity and long clustering time. In addition, the consistency between the clustering results and the original data distribution is not strong. Therefore, the multidimensional discrete big data clustering algorithm based on dynamic grid was put forward. Firstly, multidimensional discrete big data was processed in advance. The principal component analysis was used to reduce the dimension of data. The concept of entropy was introduced to divide the key attributes and noncritical attributes, so as to extract the key attributes. According to the results of data preprocessing, the dynamic grid was partitioned. According to the results, OptiGrid in data clustering algorithm was used to achieve the data clustering. The experimental results show that the clustering purity of this algorithm is between 95% and 100%, which is significantly higher than the traditional algorithm. Therefore, the multidimensional discrete big data clustering algorithm based on dynamic grid has better comprehensive performance, closer clustering shape to the original data distribution, higher clustering purity, and faster execution efficiency.
Author	Li, Xiaolei
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Copyright	Copyright © 2022 Xiaolei Li. Copyright © 2022 Xiaolei Li. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: Copyright © 2022 Xiaolei Li. – notice: Copyright © 2022 Xiaolei Li. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Snippet	Traditionally, the data clustering algorithm is lack of comprehensive performance, leading to low clustering purity and long clustering time. In addition, the...
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SubjectTerms	Algorithms Big Data Cluster analysis Clustering Communication Data mining Datasets Information technology Methods Principal components analysis Purity
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Title	Multidimensional Discrete Big Data Clustering Algorithm Based on Dynamic Grid
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