Classification of Lightning Pulses Based on Improved K-Means Clustering Algorithm
PENG Dan
ZHANG Shuxia
Abstract：K‐means has gained popularity because of its simplicity and rapid speed of classifying massive data rapidly and efficiently .However ,the output of K‐Means clustering algorithm highly depends upon the selection of initial cluster cen‐ters because the initial cluster centers are chosen randomly .The other limitation ofthe algorithm is to input the required num‐ber of clusters .This requires some sort of intuitive knowledge about appropriate value of K which is sometimes difficult to predict as it requires domainknowledge .If the value of K is not appropriate ,the output of K‐Means clustering algorithm will be bad .In this paper ,we have proposed an algorithm based on the K‐Means ,but it avoids randomly choosing of the initial cluster centers ,only setting the farthest two points in the data set as theinitial cluster centers .On the other hand ,it does not require the number of clusters K as input .It greatly reduces the user's difficulty and increases the quality of the result .This paper applys the improved K‐means clustering algorithm to the classification of natural lightning pulses and makes a compari‐son with traditional K‐means clustering algorithm .
Keywords：K-means clusteringinitial cluster centersnumber of clustersnatural lightning pulses
Publication Date：2015-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 44-47,147 )