Knowledge discovery and variable scale evaluation for long series data

Knowledge discovery and evaluation is a challenging but rewarding process of obtaining available information automatically from database. Due to the heterogeneity of the collected data, the connotative knowledge has the characteristics of uncertainty, random occurrence and variable scale. Therefore, an unsupervised knowledge discovery and variable scale evaluation model is presented in this paper based on a new multi-feature fusion method. Firstly, point at the multiple information features, an amplitude-frequency-shape based state description form is proposed in this paper. It could analyze the time series from the aspects of energy, phase, and knowledge similarity. In view of the variable number and scale of knowledge fragments, a piecewise linear segmentation criterion is put forward based on the complexity and accuracy of information representation. Then a model free knowledge discovery framework without samples labels is constructed to discover the knowledge quickly and effectively. Aimed at the variable knowledge scale, a variable scale evaluation method is first proposed to distinguish the multi-scale decision-making knowledge based on the indicators of system stability and security. It could optimize the knowledge base and guide the decision-making process. The experimental results on heterogeneous activity datasets indicate that the proposed method here could generally analysis the time series state and discover the knowledge efficiently from massive data. In addition, the knowledge discovery and evaluation at a continuous decision system show that the proposed framework could meet the needs of knowledge discovery in complex environment and effectively distinguish the knowledge to provide strong support for establishing a credible decision-making system.