Application of $K$-Means Clustering Algorithm in Energy Data Analysis

Abstract

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.

Retracted: Application of K-Means Clustering Algorithm in Energy Data Analysis

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

Enhancing K-means Clustering Results with Gradient Boosting: A Post-Processing Approach

“…MapReduce, a programming model for parallel and distributed clusters, and Hadoop, a framework for distributed processing and storage of big data [32], have been used to enhance the scalability and parallelize the execution of k-means methods, as demonstrated in several studies [5], [19], [33]. The author in [5] describes a technique for news classification that uses MapReduce and Hadoop for parallelization. It also improves the selection of the initial centroids by leveraging the organizational structure of the data.…”

“…The k-means algorithm offers several advantages, including its speed, simplicity, and ease of implementation [5]. It is useful in different applications such as marketing, recommendation systems, smart city services, the analysis of business data, and the analysis of user behaviors [6].…”

“…However, even with the advantages of the k-means algorithm, it still faces some drawbacks, such as its problem with local optima and its sensitivity to initial centroids. The k-means algorithm is sensitive to the centroids and will give different results whenever the initial centroids change [5]. In this study, we build upon a previous research paper that explored techniques for enhancing the kmeans algorithm [7].…”