| 特征融合在微博数据挖掘中的应用研究 |
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| 引用本文： | 王和勇,洪明.特征融合在微博数据挖掘中的应用研究[J].现代情报,2015,35(5):68. |
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| 作者姓名： | 王和勇 洪明 |
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| 作者单位： | 华南理工大学电子商务系, 广东 广州 510006 |
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| 摘 要： | 针对传统的微博聚类分析中,只单独针对微博阅读数、评论数等数据(下称微博结构化数据)进行分类或者单独针对由微博内容进行文本分词得到的分词数据(下称微博分词)进行分类的问题,本文采用了Kohonen聚类,研究结合微博结构化数据和微博分词的融合数据聚类的效果是否比单独对微博结构化数据或对微博分词聚类有所提高。实证数据实验结果显示,微博结构化数据单独聚类会出现一个类的标准差特别大(本文称为离群类),而对融合数据聚类,微博结构化数据则不会出现离群类;融合数据聚类结果对微博分词的影响不显著。
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The Study of Microblog Data Mining Using Feature Fusion |
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| Authors: | Wang Heyong Hong Ming |
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| Institution: | Department of E-Business, South China University of Technology, Guangzhou 510006, China |
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| Abstract: | This paper focused the problem that traditional clustering analysis have focused on only structured data such as microblog reading numbers and microblog comment numbers(microblog segmentation)or only microblog text.In this paper,microblog metadata are combined with microblog text to form fusion data and Kohonen Network Clustering is applied to test if fusion data clustering is better than microblog metadata clustering and than microblog text clustering.Experiments indicates that microblog metadata clustering may cause a class with large standard deviation(outlier class)and on the contrary,fusion data clustering does not.Microblog text clustering performs as well as fusion clustering. |
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