多媒体数据挖掘中数据间的相似性度量研究

聚类是多媒体数据挖掘的重要任务之一,数据之间的相似性度量是聚类的基础和前提。多媒体数据的特征矢量通常都是数十维甚至数百维的,但传统的相似度量方式一般只适用于低维数据。在分析高维数据特性的基础上,提出了一个新的度量方式。通过使用一个特定的策略对原始数据空间进行网格划分,该方法较好地避免了噪声数据对高维数据相似性度量的影响。实验证明此方法是有效的。

Research on Similarity Measurement in Multimedia Data Mining

Clustering is one of the focused problems in multimedia data mining,and similarity measurement among data is fundamental to clustering.In multimedia data clustering,the corresponding vector features are always of high dimensionality.Most traditional measurement methods,however,are only efficient for low dimensional data.This paper,based on an analysis of general characteristics of data presented in high dimensional spaces,proposes a new similarity measurement for multimedia data mining.It used a special strategy to split the original data space before computing the similarity among data points,thus efficiently avoiding the influence of noisy data in high dimensional dimensional spaces.Experiments show that the new method presented is effective.