| 基于对象组特征向量的聚类与分类的实现 |
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| 引用本文： | 吴萍,张利萍. 基于对象组特征向量的聚类与分类的实现[J]. 计算机工程, 2006, 32(16): 17-19,5 |
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| 作者姓名： | 吴萍 张利萍 |
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| 作者单位： | 北京理工大学计算机科学技术学院,北京,100081;兰州理工大学计算机与通信学院,兰州,730050;北京理工大学计算机科学技术学院,北京,100081 |
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| 摘 要： | 高维稀疏数据的聚类分析是目前数据挖掘领域内亟待解决的问题之一。传统的聚类方法中，大部分不适用于高维稀疏数据，不能得到满意的结果。该文借助对象组相似度和对象组的特征向量，提出了一种实现聚类的方法。根据聚类结果后，根据聚类集合的上确界和下确界给出新对象的分类。该方法思想明了，实现起来简单轻松，结果准确可靠。
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| 关 键 词： | 高维稀疏二态数据 对象组相似度 对象组特征向量 聚类 分类 |
| 文章编号： | 1000-3428（2006）16-0017-03 |
| 收稿时间： | 2005-11-27 |
| 修稿时间： | 2005-11-27 |
Realization of Object Clustering and Classification Based on OSF |
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| WU Ping,ZHANG Liping. Realization of Object Clustering and Classification Based on OSF[J]. Computer Engineering, 2006, 32(16): 17-19,5 |
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| Authors: | WU Ping ZHANG Liping |
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| Affiliation: | 1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081; 2. School of Computer and Communnication, Lanzhou University of Technology, Lanzhou 730050 |
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| Abstract: | The clustering for high-dimensional sparse data is one of important problem which need to be solved in the field of data mining.Most of the traditional clustering methods do not adapt to high-dimensional data.A clustering method is proposed based on set similarity(SS) and object set feature(OSF),and according to the supremum and infimum of clustering set,the new object can be distributed to different clusters.The idea of this kind method is very clear and easily implemented with reliable and exact results. |
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| Keywords: | High-dimensional sparse binary data Object set similarity Object set feature Clustering Classification |
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