摘要
聚类分析是数据挖掘领域中一种非常有用的技术,它用于从大量数据中寻找隐含的数据分布模式,主要有分割法、层次法、密度法、网格法和模型法等。该文主要讨论数据挖掘中一种基于密度和网格的聚类分析算法及其在客户关系管理中的应用。该算法具有较高的聚类效率而且容易实现,可以发现任意形状的聚类,时间复杂度低,聚类精度高,适用于数据的批量更新。该文还提出增量式聚类技术,它不仅能够利用前期聚类的结果,充分提高聚类分析的效率,而且可以降低维护知识库所带来的巨大开销。实验证明了算法的有效性。
Clustering analysis is a very useful tool in the domain of data mining for searching distributing mode from a great deal of data. Its main algorithms are partition-based algorithm, hierarchy-based algorithm, density-based algorithm, grid-based algorithm, and model-based algorithm. The paper mainly discusses a clustering algorithm based on density and grid in data mining, which has high clustering efficiency and low time complexity. It is efficient and effective for multi-density and uniformity density data sets with noise and suitable for batch update. After that an incremental clustering technique is presented. This technique not only makes best use of the former clustering results and improves the efficiency of clustering analysis, but also brings to the reduction of enormous expenditure on knowledge base maintenance. At last an application of the algorithm in Customer Relationship Management （CRM） is gien.
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2007年第6期1289-1291,1314,共4页
Journal of University of Electronic Science and Technology of China
关键词
聚类分析
客户关系管理
数据挖掘
密度
网格
clustering analysis
customer relationship management
data mining
density
grid
