Abstract
Clustering is a necessary data pre-processing method in data mining research, which aims to obtain the intrinsic distribution structure of valuable datasets from unlabeled datasets and thus simplify the description of the datasets. Data mining technology can discover potential and valuable knowledge from a large amount of data, giving a new meaning to the massive amount of data accumulated by people in the information age—new meaning. With the rapid development of data mining technology, as an essential part of it, grid clustering technology has been widely used in data analysis images. As a mainstream data mining method, cluster analysis facilitates data processing in machine learning by implementing clustering algorithms to analyze data and the proper operation of machines. Through the continuous experiments and proofs of previous generations, the algorithms on cluster analysis are becoming more and more mature. The text lists an overview of the evolution of high-quality clustering algorithms to understand the applications better and explore multiple clustering approaches. This study reviews papers that refer to 16 more well-known and research-discussed developmental academic articles to understand their applications on clustering and explore the development of clustering in various fields. This systematic review aims to summarize the cluster analysis and grouping techniques used to date and make recommendations for future developments.
Published Version
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