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: 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: 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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