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摘要 本文討論多維數(shù)據(jù)對象的有序聚類問題. 本文融合了樣本幾何輪廓相似度（SGSD）算法和k-均值聚類算法，構(gòu)造出“SGSD有序聚類算法”，給出了算法的一個實證分析，并同參考文獻中關(guān)于用例數(shù)據(jù)的其它算法的聚類結(jié)果進行了比較. 結(jié)果表明：本文算法將多維數(shù)據(jù)對象映射為一維結(jié)構(gòu)時信息分辨率高，且不依賴數(shù)據(jù)集之外的先驗知識和專家經(jīng)驗，實證結(jié)論符合實際情況. 翻譯的英語幫忙改一下啊 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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lidongze(RXMCDM代發(fā)): 金幣+3, 多謝應助！ 2013-12-30 21:45:57
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淺見： 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樓2013-11-07 22:28:17
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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. |
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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. 拙見 |
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