Abstract
The application of data mining technology expands various techniques in stock investment. Among them, cluster analysis is one of the common means to study stock technical indicators. There is a problem in the current cluster analysis of stock technical indicators -- the lack of validation of large-scale stock technical indicators data sets. Most of them are suitable for the comprehensive analysis of technical indicators of a single stock or multiple stocks. Aiming at this problem, this paper takes "the validity analysis of the large-scale stock KDJ index set method based on K-means clustering" as the theme. Firstly, the k-means clustering algorithm was used to construct a deep analysis model (KDJ-k-means) for the KDJ index set of the Shenzhen Index component data group. Secondly, the K, D and J index sets of 2697 constituent stocks of Shenzhen Composite Index are analyzed experimentally. Finally, the results of integrated data mining are obtained. The KDJ-k-means model is an optimization scheme based on the KDJ index set using clustering technology, which provides an intuitive and efficient visual application for deep analysis of large-scale stock data groups.
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