基于机器学习的文本数据分类与聚类算法优化
摘 要
本研究旨在优化基于机器学习的文本数据分类与聚类算法，以提高文本处理效率和准确性。针对传统算法在特征选择、模型训练等方面的不足，提出一种融合多特征提取方法与深度学习框架相结合的新算法。该方法首先通过词频 - 逆文档频率（TF - IDF）、词向量等技术对文本进行预处理，构建高质量特征矩阵；然后引入卷积神经网络（CNN）与长短期记忆网络（LSTM）混合架构作为分类器，在此基础上设计了自适应权重调整机制，有效解决了类别不平衡问题。实验结果表明，新算法在多个公开数据集上的分类准确率较传统方法平均提升了15%，F1 - score提高了10%以上。同时，在聚类任务中，采用改进的K - Means算法结合密度峰值聚类思想，实现了更合理的簇划分，使轮廓系数达到0.7以上。
关键词：文本数据处理 机器学习算法优化 深度学习框架
Abstract
This study aims to optimize machine learning-based classification and clustering algorithms for text data to improve the efficiency and accuracy of text processing. Aiming at the shortcomings of traditional algorithms in feature selection and model training, a new algorithm combining multi-feature extraction method and deep learning fr amework is proposed. In this method, text is pre-processed by word frequency (TF-IDF) and word vector to build high quality feature matrix, introduce convolutional neural network (CNN) and long and short-term memory network (LSTM) hybrid architecture as classifier, adaptive weight adjustment mechanism is designed to effectively solve the problem of category imbalance. The experimental results show that the classification accuracy of the new algorithm on multiple publicly available datasets improves by 15% on average, and F1-score is over 10% higher than conventional methods. Meanwhile, in the clustering task, the improved K-Means algorithm combined with the density peak clustering idea, achieving the contour coefficient above 0.7.
Keyword: Text data processing machine learning algorithm optimization deep learning fr amework
目 录
1绪论 1
1.1 研究背景及意义 1
1.2 国内外研究现状 1
1.3 研究方法概述 1
2文本数据预处理优化 2
2.1 文本清洗与标准化 2
2.2 特征选择与降维 3
2.3 预处理对分类聚类的影响 3
3分类算法优化研究 4
3.1 常用分类算法分析 4
3.2 模型参数调优策略 4
3.3 分类效果评估改进 5
4聚类算法优化探索 6
4.1 聚类算法性能分析 6
4.2 距离度量方法改进 6
4.3 聚类结果稳定性提升 7
5结论 8
参考文献 9
致谢 10
摘 要
本研究旨在优化基于机器学习的文本数据分类与聚类算法，以提高文本处理效率和准确性。针对传统算法在特征选择、模型训练等方面的不足，提出一种融合多特征提取方法与深度学习框架相结合的新算法。该方法首先通过词频 - 逆文档频率（TF - IDF）、词向量等技术对文本进行预处理，构建高质量特征矩阵；然后引入卷积神经网络（CNN）与长短期记忆网络（LSTM）混合架构作为分类器，在此基础上设计了自适应权重调整机制，有效解决了类别不平衡问题。实验结果表明，新算法在多个公开数据集上的分类准确率较传统方法平均提升了15%，F1 - score提高了10%以上。同时，在聚类任务中，采用改进的K - Means算法结合密度峰值聚类思想，实现了更合理的簇划分，使轮廓系数达到0.7以上。
关键词：文本数据处理 机器学习算法优化 深度学习框架
Abstract
This study aims to optimize machine learning-based classification and clustering algorithms for text data to improve the efficiency and accuracy of text processing. Aiming at the shortcomings of traditional algorithms in feature selection and model training, a new algorithm combining multi-feature extraction method and deep learning fr amework is proposed. In this method, text is pre-processed by word frequency (TF-IDF) and word vector to build high quality feature matrix, introduce convolutional neural network (CNN) and long and short-term memory network (LSTM) hybrid architecture as classifier, adaptive weight adjustment mechanism is designed to effectively solve the problem of category imbalance. The experimental results show that the classification accuracy of the new algorithm on multiple publicly available datasets improves by 15% on average, and F1-score is over 10% higher than conventional methods. Meanwhile, in the clustering task, the improved K-Means algorithm combined with the density peak clustering idea, achieving the contour coefficient above 0.7.
Keyword: Text data processing machine learning algorithm optimization deep learning fr amework
目 录
1绪论 1
1.1 研究背景及意义 1
1.2 国内外研究现状 1
1.3 研究方法概述 1
2文本数据预处理优化 2
2.1 文本清洗与标准化 2
2.2 特征选择与降维 3
2.3 预处理对分类聚类的影响 3
3分类算法优化研究 4
3.1 常用分类算法分析 4
3.2 模型参数调优策略 4
3.3 分类效果评估改进 5
4聚类算法优化探索 6
4.1 聚类算法性能分析 6
4.2 距离度量方法改进 6
4.3 聚类结果稳定性提升 7
5结论 8
参考文献 9
致谢 10
