Parallel clustering method of power marketing Big data based on DBIK means algorithm and coarse granularity

DOI

10.2991/978-94-6463-262-0_117How to use a DOI?

Keywords

DBSCAN clustering; K-means algorithm; Coarse grained calculation; Electricity marketing; Marketing Big data of electric power marketing; Parallel clustering

Abstract

By analyzing and classifying a large amount of electricity marketing data, we aim to better understand user groups and market segmentation, and provide decision-making support for power companies or suppliers. Therefore, a parallel clustering method for Big data of power marketing based on DBIK means algorithm and coarse granularity is proposed. Combining DBSCAN algorithm and K-means algorithm, DBIK means algorithm is designed to complete parallel clustering of Big data of power marketing to be clustered. Ensure that the clustering effect is coarse-grained, optimize the solution results, and realize parallel clustering of Big data of power marketing. Experimental results show that the proposed method has better parallel clustering effect of Big data of power marketing, higher clustering accuracy and shorter clustering delay.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Wei Xing
AU  - Xiujie Shi
AU  - Botao Wu
PY  - 2023
DA  - 2023/10/09
TI  - Parallel clustering method of power marketing Big data based on DBIK means algorithm and coarse granularity
BT  - Proceedings of the 3rd International Conference on Management Science and Software Engineering (ICMSSE 2023)
PB  - Atlantis Press
SP  - 1153
EP  - 1159
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-262-0_117
DO  - 10.2991/978-94-6463-262-0_117
ID  - Xing2023
ER  -