Interval Function Type Clustering Method Under Generalized Distribution
Interval function clustering is a method used to analyze continuous high-frequency data.The existing interval function based clustering under uniform distri-bution cannot fully utilize the distribution information within the interval.Moreover,the assumption of uniform distribution does not conform to the distribution of many data,resulting in poor clustering performance and stability.In response to these issues,this article considers the actual situation of data distribution.Using the mean and standard deviation of the original data,we improve the existing midpoint-radius method and propose an interval function based clustering method based on gener-alized distribution.This method expands the range of use of interval functional clustering and better describes the distribution within the interval.And it can fully utilize and obtain the inherent features of data information,improve the effectiveness and rationality of clustering results.Using the Monte Carlo method,we calculate the internal indicator and compare the advantages and disadvantages of the proposed method with existing interval function clustering under the assumption of uniform distribution.The results show that the proposed method in this article is superior to existing interval function clustering methods under uniform distribution.Finally,the proposed method in this article is applied to cluster analysis of atmospheric pol-lutant concentrations in different cities.It has been verified that this method not only effectively solves practical problems,but also has obvious advantages compared to existing methods.
Interval function datamean-standard deviation distancegeneralized distributionclustering analysis
万方数据
维普
