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Research on power marketing data mining and clustering techniques based on Bert and k-meas

Published: 31 July 2024 Publication History

Abstract

Clustering analysis is an important branch of data mining, which is applied to the power industry to improve the market competitiveness of power enterprises. This paper proposes a machine recognition algorithm KBert (Bert+K-Means) for specific type clustering of power marketing texts. The algorithm first converts the power marketing text into a high-dimensional text matrix; Secondly, iteratively optimizes key weight parameters in the Chinese Bert model to obtain a global semantic vector. Finally, in order to solve the limitations of the traditional BERT model, we introduced the K-Means algorithm to improve. The results show that the proposed KBert model overcomes the problems of long distance of power marketing text and uneven classification of sample types, and the performance index F1 value is better than the traditional BERT and Attention+Bilstm models, which realize the fast clustering recognition of multiple power marketing information with high accuracy.

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  • (2025)Accident Factors Importance Ranking for Intelligent Energy Systems Based on a Novel Data Mining StrategyEnergies10.3390/en1803071618:3(716)Online publication date: 4-Feb-2025

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    PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
    January 2024
    969 pages
    ISBN:9798400716638
    DOI:10.1145/3674225
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 31 July 2024

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    • (2025)Accident Factors Importance Ranking for Intelligent Energy Systems Based on a Novel Data Mining StrategyEnergies10.3390/en1803071618:3(716)Online publication date: 4-Feb-2025

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