Medical data stream distribution pattern association rule mining algorithm based on density estimation

Abstract

The traditional data mining method is featured by no analysis over the data distribution and incomplete derived association rule. As a result, the data mining results have the deficiencies of large redundancy probability, large root-mean-square error of approximation (RMSEA) and long consumption time. To handle these issues, this paper proposes a medical data stream distribution pattern association rule mining algorithm based on density estimation. This paper collects medical data, selects the distance method to detect abnormal orphan data in the data stream, detects the duplicate data in the data stream by the similar field matching degree, and eliminates the abnormal data and the duplicate data. Then, the data stream density is estimated based on the histogram estimation samples. According to the data density estimation results, this paper analyzes the distribution of medical data stream from perspectives of concentration, dispersion and morphological characteristics of data distribution. Afterwards, the data distribution pattern association rule mining model is constructed based on compound neural network, data distribution parameters are entered into model's clustering layer, and in-depth training is conducted over the BP (Back Propagation) neural network at the model's mining layer. Meanwhile, all rules under the combination of hidden layer's neuron activity value and corresponding output value, and all rules under the combination of hidden layer's neuron activity value and corresponding input value are derived, so as to complete association rule mining of medical data stream distribution pattern. The experimental results show that the proposed algorithm has a contour curve closest to the true probability density curve; the dispersion degree of medical data is within a reasonable range, and the medical data has high stability; the data redundancy probability is smaller; the mining result's RMSEA is small; data mining takes less time.