Open Access3D SOM Initialization Pattern Dictionary Algorithm Based on FCM Clustering
Author(s) -
Ruihua Dong,
Xueyan Zhang,
Hongsong Li
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1345/4/042052
Subject(s) - initialization , pattern recognition (psychology) , cluster analysis , computer science , set (abstract data type) , algorithm , artificial intelligence , matching (statistics) , mode (computer interface) , pattern matching , mathematics , statistics , programming language , operating system
Aiming at the problem that the three-dimensional SOM reconstruction effect of the traditional pattern dictionary initialization algorithm is sensitive to the input order of the pattern, a three-dimensional SOM initialization mode dictionary algorithm based on FCM clustering is proposed. Calculate the mean square error of the training vector set and use the FCM algorithm to aggregate the resulting mean square differences into three categories. The average values are arranged in ascending order, and a certain pattern is extracted in the training vector at the same interval to form an initial pattern dictionary. The experimental results show that the 3D SOM initialization mode dictionary algorithm based on FCM clustering reduces the search time, increases the source matching degree, and improves the overall performance of the 3D SOM algorithm.