Advanced Machine Learning on Cognitive Computing for Human Behavior Analysis
Zhihan Lv, Liang Qiao, Amit Kumar Singh
Abstract
With the increasing size of data, massive amounts of data are being generated continuously. It is hoped to find a cognitive computing technology that can effectively learn and process large-scale data. The decision tree algorithm is optimized from the perspective of machine learning. A cognitive computing model based on context-aware data flow is constructed. Classification and regression tree (CART) algorithm is used in the data computing layer of the cognitive model. In addition, the clustering effectiveness index based on frequent patterns optimizes the K-means clustering method. The performance of the algorithm is analyzed through simulation experiments. The results show that the CART algorithm requires fewer training data sets while guaranteeing classification accuracy. Also, the algorithm has obvious advantages under large-scale data. In the application of actual data set, on Over, Over+Noise, and Bridge, only the clustering validity index based on frequent pattern (FPCVI) index proposed finds the correct number of clusters. The application of DPCK-K-means clustering algorithm can ensure the accuracy and stability of behavior classification, which is greatly significant for operators to analyze user behavior and develop personalized services.