Application of K-Means Clustering Algorithm in Energy Data Analysis

In order to solve the problem of how to explore potential information in massive data and make effective use of it, this paper mainly studies news text clustering and proposes a news clustering algorithm based on improved K-Means. Then, the MapReduce programming model is used to parallelize the TIM-K-Means algorithm, so that it can run on the Hadoop platform. The accuracy and error are used as measurement indicators, and the collected datasets are used for experiments to verify the correctness and effectiveness of the TI value and TIM-K-Means algorithm. In addition, the Alibaba cloud server is used to build the Hadoop cluster, and the feasibility of parallelization transformation of TIM-K-Means algorithm is verified by accelerated comparison. The results show that the parallelized TIM-K-Means has a good acceleration ratio, can save about 30% of the time under the same conditions, and can meet the actual needs of processing massive data in the context of big data. In multidocument automatic summarization, news clustering algorithm can gather the news with the same topic and provide cleaner and accurate data for visual automatic summarization, which is of great significance in the fields of public opinion supervision, hot topic discovery, emergency real-time tracking, and so on.

Automated summary

This paper studies news text clustering and proposes a news clustering algorithm based on improved K-Means. The algorithm is parallelized using the MapReduce programming model, and the results show that the parallelized TIM-K-Means algorithm has a good acceleration ratio and can meet the needs of processing massive data in the context of big data. The news clustering algorithm is of great significance in multidocument automatic summarization.