面向自适应Web挖掘：文本数据聚类中的直方图和上下文 ][Towards Adaptive Web Mining: Histograms and Contexts in Text Data Clustering]

面向自适应Web挖掘：文本数据聚类中的直方图和上下文 Towards Adaptive Web Mining: Histograms and Contexts in Text Data Clustering


课程网址:	http://videolectures.net/ida07_klopotek_tawm/
主讲教师:	Mieczyslaw A. Klopotek
开课单位:	华沙理工大学
开课时间:	2007-10-08
课程语种:	英语
中文简介:	我们提出了一种基于增长神经气体（GNG）的高维文本数据聚类的新方法。我们通过开发一种新的基于直方图的增量模型自适应方法和稳定性评估方法，增强了我们的上下文GNG模型（之前提出将大多数计算转移到上下文敏感，局部子图和局部子空间，从而降低计算复杂性）。
课程简介:	We present a novel approach to the growing neural gas (GNG) based clustering of the high-dimensional text data. We enhance our Contextual GNG models (proposed previously to shift the majority of calculations to context-sensitive, local sub-graphs and local sub-spaces and so to reduce computational complexity) by developing a new, histogram-based method for incremental model adaptation and evaluation of its stability.
关键词:	数据聚类; 直方图; 增量模型
课程来源:	视频讲座网
最后编审:	2019-04-27：lxf
阅读次数:	60

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