半监督学习论文基于标记样本和相似度调整的k均值算法.doc

半监督学习论文：基于标记样本和相似度调整的k均值算法在文本聚类中的应用【中文摘要】在机器学习领域的众多实际应用中,获得标记样本通常需要付出较大的代价。在一些情况下,获得所有的类标记是非常困难的。近年来,半监督学习已经成为机器学习领域的一个研究热点。半监督学习同时利用标记样本和无标记样本来指导学习过程,从而获得更好的学习性能。有关半监督学习的研究可大致分为两类,即半监督分类和半监督聚类。半监督聚类也就是无监督学习,就是使用少量的标记样本对无标记样本的聚类过程进行指导。本文对聚类的相关技术和半监督学习进行了研究,介绍了文本数据的预处理、距离公式、聚类算法评估准则以及几种k-means算法的扩展算法。本文用随机选择的标记样本作为监督信息,并将标记样本转化为Must-link约束集和Cannot-link约束集,用于重构样本集合的相似度矩阵,重新确定样本之间的相似或不相似的标准。k-means+算法提供了一种有效的聚类种子的选择方法,这种方法的可以降低算法对聚类种子敏感的缺点,其聚类精度明显优于传统的随机选择种子的方法。本文在k-means+算法在初始质心的选择过程中加入了标记样本的影响,提出了一种基于标记样本和相似度调整的k-means算法,并在20-newsgroup和Spam两个数据集上进行了测试,实验结果表明本文提出的算法在聚类结果的精度和执行效率上比Seeded k-means算法和k-means+算法有更好的表现。【英文摘要】In many applications field of machine learning, the availability of data tags is usually requires more costly. In some cases, it is very difficult to access to all kinds of the class tags. In recent years, semi-supervised learning has become a research focus in the machine learning field, Semi-supervised learning taking advantage of labeled samples and unlabeled samples to guide the learning process, leading to better learning performance. Research on semi-supervised learning can be divided into two categories, namely semi-supervised classification and semi-supervised clustering. Semi-supervised clustering is to use a small amount of labeled samples and unlabeled samples to guide the clustering process. We studied the clustering of related technology and semi-supervised, introduced the text data preprocessing, distance metrics, the assessment of clustering algorithm and the k-means clustering algorithm based on the constraints.The supervised information is labeled samples selected from collection randomly, these labels are transferred into the Must-link constraint set and the Cannot-link constraints set for the reconstruction of the similarity matrix of the collection, sample re-established the standards of similar or dissimilar among samples. k-means+ algorithm provides an effective method of seeding of clustering, this approach can reduce the sensitive to initial seeds, the clustering accuracy is better than the traditional method of randomly seeding. This paper added the labels impact of the careful seeding process of k-means+ algorithm and proposed a novel k-means algorithm based on the labeled samples and adjusting similarity (LSKM). The experiments on the 20-newsgroup corpus and the Spam email collection show that LSKM consistently outperforms the Seeded k-means and k-means+on both accuracy and efficiency.【关键词】半监督学习 半监督聚类 文本聚类 k-means算法【备注】索购全文在线加好友：.3.8848 同时提供论文写作一对一指导和论文发表委托服务【英文关键词】Semi-supervised Learning Semi-supervised Clustering Text Clustering k-means algorithm【目录】基于标记样本和相似度调整的k均值算法在文本聚类中的应用摘要4-5Abstract5第一章 绪论7-111.1 研究背景于意义7-91.2 研究现状9-101.3 组织结构10-11第二章 相关工作11-192.1 文本处理11-142.1.1 文本表示与权值11-132.1.2 特征选择13-142.2 聚类14-162.3 距离公式16-172.4 聚类算法评估17-182.5 半监督聚类18-19第三章 基于标记样例和相似度调整的k-means 算法19-253.1 相关算法介绍19-223.1.1 COP k-means 算法203.1.2 Seeded k-means 算法20-213.1.3 k-means+算法21-223.2 基于标记样例和相似度调整的k-means 算法22-253.2.1 相似度调整22-233.2.2 选择聚类