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1.
为了克服经典K~Means算法随机选择初始数据中心而易陷入局部最优解和聚类结果的不确定性问题,提出一种基于粒子群和KMeans算法的改进聚类算法以实现移动用户分类。首先,定义数据对象密度并采用改进的普里姆算法初始化聚类中心,然后,将此聚类中心用于初始化粒子位置,采用混沌粒子群算法寻优获得最优解作为最终的聚类中心,最后,采用经典K—Means算法根据最终聚类中心进行聚类。仿真实验表明文中方法能正确地实现移动用户分类,并具有较强的全局寻优能力和较快的收敛速度,弥补了经典K—Means方法的不足,具有较强的现实意义。  相似文献   

2.
基于类信息的文本聚类中特征选择算法   总被引:2,自引:0,他引:2  
文本聚类属于无监督的学习方法,由于缺乏类信息还很难直接应用有监督的特征选择方法,因此提出了一种基于类信息的特征选择算法,此算法在密度聚类算法的聚类结果上使用信息增益特征选择法重新选择最有分类能力的特征,实验验证了算法的可行性和有效性。  相似文献   

3.
一种用于Web文本聚类的特征选择方法   总被引:1,自引:0,他引:1  
特征选择已经广泛地应用在文本分类和文本聚类中,相对于无监督的特征选择方法,有监督的特征选择方法在过滤噪音等方面更为有效.但是,由于缺少类标签,它很难应用到文本聚类中.提出了一种针对Web文本聚类的新的特征选择算法--基于k-means的多特征联合选择算法(MFCC).MFCC充分利用了一个特征空间的中间聚类结果来帮助另一个特征空间进行特征选择.实验证明,MFCC有效地提高了聚类质量.  相似文献   

4.
基于QPSO的数据聚类*   总被引:1,自引:0,他引:1  
在KMeans聚类、PSO聚类、KMeans和PSO混合聚类(KPSO)的基础上,研究了基于量子行为的微粒群优化算法(QPSO)的数据聚类方法,并提出利用KMeans聚类的结果重新初始化粒子群,结合QPSO的聚类算法,即KQPSO。介绍了如何利用上述算法找到用户指定的聚类个数的聚类中心。聚类过程都是根据数据之间的Euclidean(欧几里得)距离。KMeans算法、PSO算法和QPSO算法的不同在于聚类中心向量的“进化”上。最后使用三个数据集比较了上面提到的五种聚类方法的性能,结果显示基于QPSO  相似文献   

5.
采用K—Means聚类算法和神经网络算法,对药物的使用进行建模.通过模型可得到不同临床特征病人更适合服用何种药物的建议结果,正确率高达94.7%,实现用药的合理化。  相似文献   

6.
基于K Means的文本层次聚类算法研究   总被引:6,自引:0,他引:6  
尉景辉  何丕廉  孙越恒 《计算机应用》2005,25(10):2323-2324
提出了一种基于K-Means的文本层次聚类算法。它结合凝聚层次聚类和K Means算法的特点,减少凝聚层次法在凝聚过程中的错误,提高了聚类质量。实验结果表明,该算法的聚类质量优于层次聚类法。  相似文献   

7.
数据挖掘中的聚类分析对发现数据中隐含的类别和分布有着重要的应用。传统的K—Means聚类算法在给出簇数目的条件下能够对数据进行较好的聚类,算法采用批量模式进行学习,在每一趟数据扫描结束后更新簇中心。序列模式是另外一种学习方式,它每扫描一条记录就更新簇中心。本文提出并实现了基于序列模式的K—Means算法,并与采用批量模式的K—Means算法进行了比较。  相似文献   

8.
对教学反思内容的准确评估是教师基于教学反思过程提升其专业能力的重要保障。基于改进的K-Means算法对相同主题的教学反思文本进行聚类,通过给定初始聚类中心K的取值范围使其可以在给定范围内自动增加,在聚类过程中加入相似度阈值以限定文本间相似度的取值范围,实现对教学反思文本的分类和对自我反思文本的定位。实验结果表明改进的K—Means算法在反思文本聚类的准确率和稳定性方面比传统算法有所提高,且能根据教学反思内容准确地进行自动分类。  相似文献   

9.
陈晓春 《福建电脑》2009,25(2):79-80
本文叙述了K—Means算法和EM算法的基本原理及性质,通过聚类实验对二者稳定性和时间复杂度进行了比较分析。  相似文献   

10.
聚类算法作为发现数据内在结构与分布特征的无监督学习方法,被广泛应用于各个领域。伴随着互联网的高速发展和在线文档数量的大幅增加,文本聚类已成为一项重要任务。讨论文本聚类算法的基本概念与应用场景,对文本聚类算法及评价方法进行综述。  相似文献   

11.
Multitype features coselection for Web document clustering   总被引:2,自引:0,他引:2  
Feature selection has been widely applied in text categorization and clustering. Compared to unsupervised selection, supervised feature selection is more successful in filtering out noise in most cases. However, due to a lack of label information, clustering can hardly exploit supervised selection. Some studies have proposed to solve this problem by "pseudoclass." As empirical results show, this method is sensitive to selection criteria and data sets. In this paper, we propose a novel feature coselection for Web document clustering, which is called multitype features coselection for clustering (MFCC). MFCC uses intermediate clustering results in one type of feature space to help the selection in other types of feature spaces. Our experiments show that for most selection criteria, MFCC reduces effectively the noise introduced by "pseudoclass," and further improves clustering performance.  相似文献   

12.
Feature selection is an important method for improving the efficiency and accuracy of text categorization algorithms by removing redundant and irrelevant terms from the corpus. In this paper, we propose a new supervised feature selection method, named CHIR, which is based on the chi2 statistic and new statistical data that can measure the positive term-category dependency. We also propose a new text clustering algorithm, named text clustering with feature selection (TCFS). TCFS can incorporate CHIR to identify relevant features (i.e., terms) iteratively, and the clustering becomes a learning process. We compared TCFS and the K-means clustering algorithm in combination with different feature selection methods for various real data sets. Our experimental results show that TCFS with CHIR has better clustering accuracy in terms of the F-measure and the purity.  相似文献   

13.
为了有效利用多视图数据信息提升监督特征选择的性能,构建了一种结构化多视 图稀疏限定,并基于该稀疏限定提出了一种监督特征选择方法,即结构化多视图监督特征选择 方法(SMSFS)。该方法在特征选择过程中能够同时考虑不同视图特征的重要性以及同一视图中 不同特征的重要性,从而有效的结合多视图数据信息,提升监督特征选择的性能。SMSFS 目标 函数是非凸的,设计了一个有效的迭代算法对目标函数进行求解。将所提结构化多视图监督特 征选择方法 SMSFS 应用到了图像标注任务,在 NUS-WIDE 和 MSRA-MM2.0 图像数据库上进 行了实验,并与其他特征选择算法进行了比较,实验结果表明该算法能够有效结合多视图数据 信息,提升特征选择性能。  相似文献   

14.
针对K-Means算法在文本聚类过程中易陷入局部最优,造成文本聚类结果不准确的问题,提出了一种基于改进灰狼优化算法的K-Means文本聚类方法。在对文本数据进行分词、去停用词、特征提取以及文本向量化后,通过免疫克隆选择选出精英个体,并对精英个体进行深度探索以增加灰狼种群的多样性,避免早熟收敛现象的发生;将粒子群位置更新思想与灰狼位置更新结合,降低灰狼优化算法陷入局部极值的风险;与K-Means算法结合进行文本聚类。所提算法与K-Means算法、GWO-KMeans以及IPSK-Means算法相比,其准确率、召回率和F值平均都有明显提高,文本聚类结果更可靠。  相似文献   

15.
Wu  Yue  Wang  Can  Zhang  Yue-qing  Bu  Jia-jun 《浙江大学学报:C卷英文版》2019,20(4):538-553

Feature selection has attracted a great deal of interest over the past decades. By selecting meaningful feature subsets, the performance of learning algorithms can be effectively improved. Because label information is expensive to obtain, unsupervised feature selection methods are more widely used than the supervised ones. The key to unsupervised feature selection is to find features that effectively reflect the underlying data distribution. However, due to the inevitable redundancies and noise in a dataset, the intrinsic data distribution is not best revealed when using all features. To address this issue, we propose a novel unsupervised feature selection algorithm via joint local learning and group sparse regression (JLLGSR). JLLGSR incorporates local learning based clustering with group sparsity regularized regression in a single formulation, and seeks features that respect both the manifold structure and group sparse structure in the data space. An iterative optimization method is developed in which the weights finally converge on the important features and the selected features are able to improve the clustering results. Experiments on multiple real-world datasets (images, voices, and web pages) demonstrate the effectiveness of JLLGSR.

  相似文献   

16.
王宏杰  师彦文 《计算机科学》2017,44(Z11):457-459, 502
为了提高传统K-Means聚类算法的聚类准确性,提出一种结合初始中心优化和特征加权的改进K-Means聚类算法。首先,根据样本特征对聚类的贡献程度获得初始特征权重,构建一种加权距离度量。其次,利用提出的初始聚类中心选择方法获得k个初始聚类中心,并结合初始特征权重进行初步聚类。然后,根据聚类精度来调整特征权重并再次执行聚类过程。重复执行上述过程直到聚类精度不再变化,获得最终的聚类结果。在UCI数据库上的实验结果表明,与现有相关K-Means聚类算法相比,该算法具有较高的聚类准确性。  相似文献   

17.
将传统的文本相似度量方法直接移植到短文本时,由于短文本内容简短的特性会导致数据稀疏而造成计算结果出现偏差。该文通过使用复杂网络表征短文本,提出了一种新的短文本相似度量方法。该方法首先对短文本进行预处理,然后对短文本建立复杂网络模型,计算短文本词语的复杂网络特征值,再借助外部工具计算短文本词语之间的语义相似度,然后结合短文本语义相似度定义计算短文本之间的相似度。最后在基准数据集上进行聚类实验,验证本文提出的短文本相似度计算方法在基于F-度量值标准上,优于传统的TF-IDF方法和另一种基于词项语义相似度的计算方法。  相似文献   

18.

Text document clustering is used to separate a collection of documents into several clusters by allowing the documents in a cluster to be substantially similar. The documents in one cluster are distinct from documents in other clusters. The high-dimensional sparse document term matrix reduces the clustering process efficiency. This study proposes a new way of clustering documents using domain ontology and WordNet ontology. The main objective of this work is to increase cluster output quality. This work aims to investigate and examine the method of selecting feature dimensions to minimize the features of the document name matrix. The sports documents are clustered using conventional K-Means with the dimension reduction features selection process and density-based clustering. A novel approach named ontology-based document clustering is proposed for grouping the text documents. Three critical steps were used in order to develop this technique. The initial step for an ontology-based clustering approach starts with data pre-processing, and the characteristics of the DR method are reduced with the Info-Gain collection. The documents are clustered using two clustering methods: K-Means and Density-Based clustering with DR Feature Selection Process. These methods validate the findings of ontology-based clustering, and this study compared them using the measurement metrics. The second step of this study examines the sports field ontology development and describes the principles and relationship of the terms using sports-related documents. The semantic web rational process is used to test the ontology for validation purposes. An algorithm for the synonym retrieval of the sports domain ontology terms has been proposed and implemented. The retrieved terms from the documents and sport ontology concepts are mapped to the retrieved synonym set words from the WorldNet ontology. The suggested technique is based on synonyms of mapped concepts. The proposed ontology approach employs the reduced feature set in order to clustering the text documents. The results are compared with two traditional approaches on two datasets. The proposed ontology-based clustering approach is found to be effective in clustering the documents with high precision, recall, and accuracy. In addition, this study also compared the different RDF serialization formats for sports ontology.

  相似文献   

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