Chaos sequence data classification mining research under network visualization analysis. Issue 2 (17th February 2020) - Hou, Yan
ABSTRACT: Network visualization analysis is a method of quantitative analysis of data in network services by using visualization techniques. However, it is difficult to classify the multimodal underlying sequence characteristics of the data by using the current method for the chaos sequence data classification mining; there is a problem of the large error of the classification of chaotic sequence data. In the article, this method is a method of network visualization analysis of chaotic sequence data classification mining based on multimodal subspace correlation transmission. This method uses the hidden Markov model to convert the chaotic sequence data into the likelihood space, the magnitude of the likelihood is identified by the symmetry Kullback–Leibler distance, extracting the underlying characteristics of multiple modal states, and carry out the transmission of the correlation between video data multimodal subspace. The accuracy and the mean square error were used to define the performance of the chaotic sequence data classification. The classification accuracy of this method is about 90%, and the root mean square error is less than 0.03, which greatly improves the classification accuracy. The packet loss rate of literature [Zheng B, Su H, Lin L. Application of unsupervised feature selection in time series data mining. Chin J Sci Instrum. 2014;35(4):834–840] is about 60%, the missing rate of literature [Mengli R. Algorithm of web hot data mining based on structured segmentation. Bull Sci Technol. 2015;4:115–117] is 50%, and the packet loss rate of this method is about 20%, which proves that this method has higher classification accuracy and application value, and is more suitable for other sequence data mining applications. The similarity between the time sequence data is given and reduces the dimension of the original data in order to obtain the coordinates module in the lower dimensional sequence data space, and then complete network visualization analysis to analyze chaos sequence data mining. The experimental simulation shows that the proposed method has a higher classification precision and greatly expands the application domain of the sequential data classification mining.