"基于深度学习的高维数据聚类算法研究"

Abstract As information technology continues to advance rapidly, data sets are becoming larger and more complex, often existing in high-dimensional spaces. Clustering algorithms play a crucial role in data analysis, helping to group similar data points together. However, traditional clustering algorithms face challenges when working with high-dimensional data, such as dealing with irrelevant attributes, sparse data distribution, and computational complexity. This research focuses on developing a high-dimensional data clustering algorithm based on deep learning techniques to address these challenges. Deep learning has shown great success in various fields, such as image recognition and natural language processing, by extracting hierarchical features from raw data. By leveraging the power of deep learning, we aim to improve the performance of clustering algorithms on high-dimensional data sets. The proposed algorithm will be designed to effectively handle the high dimensionality of the data, automatically identify relevant features, and optimize the clustering process. Through extensive experimentation and evaluation, we will demonstrate the effectiveness of the algorithm in accurately clustering high-dimensional data sets. Overall, this research aims to contribute to the field of data clustering by integrating deep learning techniques with high-dimensional data analysis. By developing a more robust and efficient clustering algorithm, we can better address the challenges posed by modern data sets and improve the quality of data analysis in various application domains.