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摘要 本文讨论多维数据对象的有序聚类问题. 本文融合了样本几何轮廓相似度（SGSD）算法和k-均值聚类算法，构造出“SGSD有序聚类算法”，给出了算法的一个实证分析，并同参考文献中关于用例数据的其它算法的聚类结果进行了比较. 结果表明：本文算法将多维数据对象映射为一维结构时信息分辨率高，且不依赖数据集之外的先验知识和专家经验，实证结论符合实际情况. 翻译的英语帮忙改一下啊 Abstract:This paper discusses the problems on the ordered clustering algorithm for multidimensional data objects. Combining the sample geometry similarity algorithm (SGSD) and the k-means clustering algorithm, the SGSD ordered clustering algorithm is proposed, and exampled. And compares with the results of other references which use the same datas. The conclusion shows that,when the algorithm maps the multidimensional data object for one dimensional,the information resolution is high. it does’t depend on the datas set of prior knowledges and expert experiences which is outside the datas set ,the empirical conclusion of the algorithm conforms to the actual. |
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befair
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RXMCDM: 2013-12-30 21:45:29
lidongze(RXMCDM代发): 金币+3, 多谢应助！ 2013-12-30 21:45:57
RXMCDM: 2013-12-30 21:45:29
lidongze(RXMCDM代发): 金币+3, 多谢应助！ 2013-12-30 21:45:57
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浅见： Abstract:This paper discusses some topics related to the ordered clustering algorithm for multidimensional data objects. Integrating the sample geometry similarity algorithm (SGSD) and the k-means clustering algorithm, the SGSD ordered clustering algorithm is proposed and an example is given. Comparison is made with results in the references using other algorithms on the same data. The results show that, the resolution is high when using the newly proposed algorithm to map multidimensional data object onto one dimensional one. Moreover, the approach does not rely on any prior knowledge or expert experience than the data set itself and the the example shows a good agreement with the actual case. |
2楼2013-11-07 22:28:17
phu_grassman
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| Abstract: Ordered clustering algorithm was discussed for multidimensional data objects in this paper. The SGSD ordered clustering algorithm was proposed based on the sample geometry similarity algorithm (SGSD) and the k-means clustering algorithm, and a practical analysis was performed. Besides, a comparison was made with the results of other algorithms from the references which use the same data. It shows that high information resolution can be achieved when the algorithm maps the multidimensional data object for one dimension, which is independent of any prior knowledge or expertise outside the data set. The empirical conclusion agrees with the actual conditions. |
3楼2013-11-07 22:42:22
stingdhk
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4楼2013-11-08 10:26:35
孔老三
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5楼2013-11-08 16:41:22
deng神之刺客
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lidongze(RXMCDM代发): 金币+1, 多谢应助！ 2013-12-30 21:41:58
lidongze(RXMCDM代发): 金币+1, 多谢应助！ 2013-12-30 21:41:58
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Abstract: The problems of the ordered clustering algorithm for multidimensional data objects was discussed in this article. The sample geometry similarity algorithm (SGSD) and the k-means clustering algorithm were fused in the study, the SGSD ordered clustering algorithm was built, a demonstration analysis of algorithm was given. Besides, it was compared to the results of other algorithms from the references which use the same data. The results showed that a high information resolution could be obtained when the algorithm maps the multidimensional data object for one dimension, and it wouldn't rely on priori knowledge and expert experience out of data sets, empirical conclusion conformed to the actual conditions. 拙见 |
6楼2013-11-08 20:39:53
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