Data Mining Algorithm for Wireless Sensor Networks Based on Subspace Heterogeneous Fusion Matching

In order to improve the data mining ability of wireless sensor network communication link, a data mining method of multi-dimensional node combination wireless sensor network communication link based on subspace heterogeneous fusion matching is proposed. The fuzzy information detection model of multi-dimensional node combined wireless sensor network big data is constructed, and the statistical analysis of multi-dimensional node combined wireless sensor network big data is carried out by combining linear balanced scheduling analysis method. According to the feature extraction results of multi-dimensional node combined wireless sensor network big data, the data fusion of multi-dimensional node combined wireless sensor network communication link is carried out by using linear balanced scheduling method, and the feature clustering model of wireless sensor network data is established by combining subspace heterogeneous fusion method. The simulation results show that this method has higher accuracy and better feature resolution, and improves the heterogeneous stability of the multi-dimensional node combination wireless sensor network.