K-means clustering of generally distributed interval symbolic data
Wenhua Li · Guanli kexue xuebao · 2013
The existed clustering methods of interval data mostly supposed that the data are uniformly distributed across the interval.However,this is not always practical.Taking this into account,this paper aims to research the k-means clustering method of interval data with a general distribution.The definition of generally distributed interval data is proposed,and descriptive statistics was researched based on empirical distribution theory.On the basis of Hausdorff distance,the paper puts forward a new distance for interval data,which considers the point data contained in the intervals.Based on this,we present a algorithm of k-means clustering of generally distributed interval symbolic data.A simulation experiment is conducted to evaluate the validity of our method.The results show that,compared with analysis methods of uniform interval symbolic data,the analysis methods of generally distributed interval symbolic data are more effective under all the conditions designed in our experiment.Finally,the method is illustrated by an example of real-case data which shows the advantages of our method in the practical application.