Affiliations: [a]
Rongzhi College of Chongqing Technology and Business University, Chongqing, China
| [b] School of Public Health and Management, Chongqing Medical University, Chongqing, China
| [c] Department of Economics, Pusan National University, Pusan, Korea
Correspondence:
[*]
Corresponding author. Weiwei Liu, School of Public Health and Management, Chongqing Medical University, Chongqing 400016, China. E-mail: [email protected].
Abstract: At present, KDD research covers many aspects, and has achieved good results in the discovery of time series rules, association rules, classification rules and clustering rules. KDD has also been widely used in practical work such as OLAP and DW. Also, with the rapid development of network technology, KDD research based on WEB has been paid more and more attention. The main research content of this paper is to analyze and mine the time series data, obtain the inherent regularity, and use it in the application of financial time series transactions. In the financial field, there is a lot of data. Because of the huge amount of data, it is difficult for traditional processing methods to find the knowledge contained in it. New knowledge and new technology are urgently needed to solve this problem. The application of KDD technology in the financial field mainly focuses on customer relationship analysis and management, and the mining of transaction data is rare. The actual work requires a tool to analyze the transaction data and find its inherent regularity, to judge the nature and development trend of the transaction. Therefore, this paper studies the application of KDD in financial time series data mining, explores an appropriate pattern mining method, and designs an experimental system which includes mining trading patterns, analyzing the nature of transactions and predicting the development trend of transactions, to promote the application of KDD in the financial field.
Keywords: Information entropy, data mining, financial time series, clustering, multimedia
