Rapid Integration Algorithm for Music Education Information Resources Based on Data Mining

This paper addresses the critical and complex task of integrating music education information resources, highlighting the scattered nature and inefficient utilization of current resources in the field. The significant challenge of effectively consolidating diverse types of music education resources, such as digital audio, scores, and instructional videos, is addressed. We propose a novel algorithm, rooted in data mining techniques, specifically designed for the rapid integration of these resources. Our method involves a systematic approach that begins with standardizing the format of the input data, including audio lengths and image dimensions, to ensure uniformity. We then employ Convolutional Neural Network technology to extract features from audio, images, and videos, harnessing the power of deep learning to handle the multi-modality of the data. The extracted features from these varied sources are integrated into a unified format for subsequent processing. Following the feature extraction and integration, we utilize spectral clustering to categorize the music education resources. This clustering method is particularly effective in dealing with the complexities and nuances of the multi-modal data. Our experimental results demonstrate the efficacy of our algorithm in accurately classifying and integrating diverse music education resources, offering a promising solution to the challenges currently faced in the field.