An Immune Genetic K-Means Algorithm for Mongolian Elements Clustering

被引：0
|
作者：
Hua, Chun [1 ,2 ]
Cheng, Chun Ying [1 ]
机构：
[1] Inner Mongolia Univ Nationalities, Sch Comp Sci & Technol, Tongliao 028043, Peoples R China
[2] Dalian Univ Technol, Sch Math Sci, Dalian 116024, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2018 | 2018年 / 10878卷
关键词：
Immune algorithm; Genetic algorithm; Mongolian element clustering; K-Means;
D O I：
10.1007/978-3-319-92537-0_32
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Text clustering is an important area in artificial intelligence. Production of the some character recognitions have been transformed into commercial soft-ware, but the research on Mongolian elements is now just beginning. There are many characters in Mongolian structure and written pattern in contrast with other kinds of characters. In this paper, we proposed a novel clustering technique that combined genetic K-Means algorithm and immune algorithm. The proposed technique clustered the Mongolian elements to the better result. Experiment show that the accurate clustering rate of this method is over 98% and this technique is efficient and feasible.
引用
收藏
页码：273 / 278
页数：6
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