How to organize data with measurement errors?

Clustering analysis is an outstanding contribution to data mining and knowledge discovery from large-scale data, which has become an important research direction. Many excellent researches have been developed, whereas there is a blind point which is not addressed. How to organize data with measurement errors? What is needed for this purpose is the concept of error number- an error number which is defined as a(x)±α_a(x), where a(x) is the measurement value of an object x under an attribute a, and α_a(x) is the value of its measurement error. In this paper, we will explore tentatively data clustering with measurement errors through employing the framework of k-means clustering algorithm, which can be regarded as an introductory work.