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gmeans-- Clustering with first variation and splitting
文本聚类算法Gmeans ,使用了3种相似度函数,cosine,euclidean ,KL.文本数据使用的是稀疏矩阵形式.
-gmeans clustering with first variation and splitting
Gmeans,a text clustering algorithm, uses 3 functions,cosine,euclidean and
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DBSCAN算法的JAVA实现,可以在D:\\使用text.txt数据文件来进行DBSCAN聚类-DBSCAN algorithm JAVA, the D : \\ text.txt data files used for clustering DBSCAN
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主要是数据挖掘中的文本挖掘算法及其分析,其中包括层次聚类,空间向量模型等,处理对象有对于网页的也有针对纯文本的。,Mainly in data mining and analysis of text mining algorithms, including hierarchical clustering, vector space model to deal with subjects of the pages are for plain text.
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文本聚类分类数据集 包括20newsgroup 和retuers 中抽取的500条数据,有四个表-Text clustering and classification of data sets including 20newsgroup retuers 500 extracted data, there are four tables
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基于opencv的kmeans聚类实现
输入文本数据,进行聚类输出-Opencv-based clustering of kmeans the input text data, clustering output
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此程序实现了如何在TXT或WORD文档中进行数据挖掘,在文本中提取有用信息-The realization of this procedure how to TXT or WORD document to carry out data mining, in the text to extract useful information
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KAV是利用Visual C++ 6.0编写的一个小程序,能实现对特定数据结果的文本数据进行聚类分析,所使用的聚类方法是K均值。
-KAV is the use of Visual C++ 6.0 to prepare a small procedure to achieve the outcome of specific data on the text data clustering analysis, the use of the K-means clustering method.
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files that are very useful as data sets for document clustering.. i have done project based on these document sets for my pg degr-files that are very useful as data sets for document clustering.. i have done project based on these document sets for m
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基于WEKA平台的文本聚类研究与实现
文本聚类是文本挖掘领域的一个重要研究分支,是聚类方法在文本处理领域的应用。本文对基于空间向量模型的文本聚类过程做了较深入的讨论和总结,利用文本语料库,基于数据挖掘工具研究并实现了文本聚类的过程。本文首先给出了文本聚类的思想和过程,回顾了文本聚类领域的已有成果,列举了文本聚类领域在特征表示、特征提取等方面的基础研究工作。另外,本文回顾了现有的文本聚类算法,以及常用的文本聚类效果评价指标。在研究了已有成果的基础上,本文利用20 Newsgroup文本语料库,
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流形学习,局部线性嵌入式算法(LLE),一种智能的算法去推测捕捉高维空间中所包含的低维特征。与适合于局部维数约减的聚类算法不同,LLE算法在单一的低维的全域坐标系统中表征采样空间,然而它并没有优化最小局域。通过对线性重构的局域对称的研究应用,LLE能够描述非线性流形的全局结构,例如那些人脸的数据集或者文本文档集-Manifold learning, embedded local linear algorithm (LLE), an intelligent algorithm to predict
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ector quantization is a classical quantization technique from signal processing which allows the modeling of probability density functions by the distribution of prototype vectors. It was originally used for data compression. It works by dividing a l
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将文本文件中数据按照C均值聚类算法进行聚类-The text file data according to C-means clustering algorithm for clustering
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AP聚类算法的C++代码实现,其中数据是文本读入iris数据,P值选取欧式矩阵最小值。显示结果为聚类后结果-AP clustering algorithm C++ code, in which data is read into the text iris data, P values selected European matrix minimum. Showing results clustering results after
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数据挖掘的聚类算法实现
Implementation of text clustering algorithms including K-means, MBSAS, DBSCAN-data mining cluster
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基于文本数据的投票k-means聚类融合算法的实现-Voting k-means clustering text-based data fusion algorithm implementation
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k-means算法是文本聚类经典算法,也是数据挖掘十大经典算法之一。k-means算法Java实现。-k-means algorithm is a classical algorithm text clustering, data mining is one of the ten classic algorithms. k-means algorithm is implemented in Java.
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DBSCAN算法;读取文本数据;聚类表示;简单快速-DBSCAN algorithm read text data clustering representation simple and fast
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这里会用到TF/IDF权重,用余弦夹角计算文本相似度,用方差计算两个数据间欧式距离,用k-means进行数据聚类等数学和统计知识。-Here will use the TF/IDF weight, with cosine angle calculation of text similarity, with the variance of the two data between the data of the European distance, with K-means data cluste
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PDF格式的PPT,来自英国南安普顿大学。主要介绍了数据挖掘的技术以及应用,包括决策树,推荐系统,文本聚类,搜索引擎,购物篮子分析。-PPT PDF format, the University of Southampton. It introduces data mining technology and applications, including decision, recommendation systems, text clustering, search engines, sho
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机器学习中文本聚类算法,里面有5个文件,包含Python实现代码和测试数据。-The clustering algorithm machine learning Chinese, there are five files that contain Python implementation code and test data.
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