Description: 数据挖掘算法之一，基于代表点的CURE聚类算法，该算法先把每个数据点看成一类，然后合并距离最近的类，直至类个数为所要求的个数为止。-CURE cluster algorithm based on representive point,one of data mining algorithms,classifies each data as a category firstly, then unifies categories with the nearest distance into one until the number of class is coincidence with the classes demanded. Platform: |
Size: 45056 |
Author:黄镇 |
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Description: Solaris环境下的数据挖掘算法：birch聚类算法。该算法适用于对大量数据的挖掘。-Solaris environment data mining algorithms : birch clustering algorithm. The algorithm is applicable to the large volume of data mining. Platform: |
Size: 1319936 |
Author:npu |
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Description: Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
一个可以实现多种方法分类的软件，利用各个
对象的属性。决策树，距离、密度等-Weka is a collection of machine learning al gorithms for data mining tasks. The algorithms can either be applied directly to a dataset or ca lled from your own Java code. Weka contains tool 's for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for d eveloping new machine learning schemes. can be a real Categories are various methods of software, using all the attributes of objects. Decision Tree, distance, density, etc. Platform: |
Size: 15446016 |
Author:马何坛 |
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Description: 两个som聚类算法，可以用于图象处理，数据挖掘，运行感觉不错，推荐！值得收藏！-two som clustering algorithm can be used in image processing, data mining, running good feeling recommended! Worth collecting! Platform: |
Size: 653312 |
Author:刘鹏 |
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Description: 次算法为数据挖掘中动态聚类算法的实现，对数据挖掘有一定的帮助-time data mining algorithms for dynamic clustering algorithm to the realization of data mining is certainly helpful Platform: |
Size: 243712 |
Author:水蓝 |
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Description: 数据挖掘领域中聚类分析的相关的PPT以及一个聚类算法介绍-field of data mining cluster analysis of the PPT, and a clustering algorithm introduced Platform: |
Size: 1645568 |
Author:qiaoyu |
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Description: 数据挖掘算法。K-Means聚类数据挖掘算法。该算法是用Java语言编写的。-Data mining algorithms. K-Means clustering algorithm for data mining. The algorithm is a Java language. Platform: |
Size: 41984 |
Author:张志娟 |
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Description: 介绍一种非常有效的对大数据库的数据挖掘算法-数据聚类-A very effective introduction to the large database of data mining algorithms- Data Clustering Platform: |
Size: 1482752 |
Author:huwenan |
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Description: 将Weka数据挖掘工具所产生的K-MEANS和DBSCAN结果转化成MATLAB可输出三维图像的格式-Weka data mining tools will be generated by K-MEANS and DBSCAN results can be exported into MATLAB three-dimensional image format Platform: |
Size: 1006592 |
Author:王坤 |
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Description: 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 Platform: |
Size: 384000 |
Author:sam |
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Description: 数据挖掘算法的实现，基于模糊聚类的最大树算法，数据集是darpa99,也就是KDD-CUP99中采用的数据集-The realization of data mining algorithms, based on fuzzy clustering of the largest tree algorithm, a data set is darpa99, which is used in KDD-CUP99 data set Platform: |
Size: 32768 |
Author:谢松林 |
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Description: 软件学报 2008年论文《聚类算法研究》，作者孙吉贵, 刘杰, 赵连宇。pdf格式，14页。对近年来聚类算法的研究现状与新进展进行归纳总结.一方面对近年来提出的较有代表性的聚类算法,从算法思想、关键技术和优缺点等方面进行分析概括 另一方面选择一些典型的聚类算法和一些知名的数据集,主要从正确率和运行效率两个方面进行模拟实验,并分别就同一种聚类算法、不同的数据集以及同一个数据集、不同的聚类算法的聚类情况进行对比分析.最后通过综合上述两方面信息给出聚类分析的研究热点、难点、不足和有待解决的一些问题.上述工作将为聚类分析和数据挖掘等研究提供有益的参考.
-The research actuality and new progress in clustering algorithm in recent years are summarized in this
paper. First, the analysis and induction of some representative clustering algorithms have been made from several
aspects, such as the ideas of algorithm, key technology, advantage and disadvantage. On the other hand, several
typical clustering algorithms and known data sets are selected, simulation experiments are implemented from both
sides of accuracy and running efficiency, and clustering condition of one algorithm with different data sets is analyzed by comparing with the same clustering of the data set under different algorithms. Finally, the research hotspot, difficulty, shortage of the data clustering and some pending problems are addressed by the integration of the aforementioned two aspects information. The above work can give a valuable reference for data clustering and data mining. Platform: |
Size: 470016 |
Author:dengyue |
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