Computer Science ›› 2017, Vol. 44 ›› Issue (Z6): 424-427.doi: 10.11896/j.issn.1002-137X.2017.6A.095

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Improved Adaptive Spectral Clustering NJW Algorithm

LI Jin-ze, XU Xi-rong, PAN Zi-qi and LI Xiao-jie   

  • Online:2017-12-01 Published:2018-12-01

Abstract: Clustering algorithm is a new research hotspot in the field of machine learning in recent years.In order to cluster in the sample space of any shape,the scholars have put forward excellent algorithms such as spectral clustering and graph theory clustering.In this paper,we first introduced the basic idea of the classical NJW algorithm of graph theo-ry clustering algorithm and NeiMu algorithm,and then we gave a new algorithm named Improved adaptive spectral clustering NJW algorithm.This approach overcomes the shortcomings of the classical NJW algorithm,which needs to adjust the number of clusters in advance and needs to be repeated to obtain the data classification results.We compared the adaptive NJW algorithm with the classical NJW algorithm,the adaptive NJW algorithm and the NeiMu graph theory clustering algorithm on the UCI standard data set and the measured data set.The experimental results show that the adaptive NJW algorithm is convenient and well practicable.

Key words: Spectral clustering,Graph theory clustering,Intrinsic gap

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