Data Processing Method Based on Incremental Weight Sliding Window and KD-Tree

The explosive growth of data volume has brought about problems such as data processing accuracy and efficiency, and data application scenarios. This article aims to study the necessity and feasibility of data governance and propose a set of data governance solutions. By analyzing the characteristics and management complexity of data, the methods are proposed to improve the application of data and promote the overall efficiency and safety of intelligent application systems. Therefore, this article proposes a data processing optimization method for real data processing and application services. The main work is as follows: Firstly, to solve the problem of convergence judgment accuracy of real data processing algorithms, a MiniBatch-KMeans algorithm based on sliding window is proposed, which suppresses the randomness of SSE changes, improves the accuracy of judging the timing of algorithm iteration termination, and improves the effectiveness of the algorithm. Secondly, in view of the problem of data processing efficiency, the KD-Tree parallel data structure is introduced to speed up the query process of the cluster center, improve the search efficiency of the algorithm, and optimize the data structure of the search and update process. Finally, by using parallel tasks, the convergence speed of the algorithm is effectively improved and the data processing efficiency is improved.