Development of an efficient hierarchical clustering analysis using an agglomerative clustering algorithm

被引:9
|
作者
Naeem, Arshia [1 ]
Rehman, Mariam [2 ]
Anjum, Maria [1 ]
Asif, Muhammad [3 ]
机构
[1] Lahore Coll Women Univ, Dept Comp Sci, Lahore 54000, Pakistan
[2] Govt Coll Univ Faisalabad, Dept Informat Technol, Faisalabad 38000, Pakistan
[3] Natl Text Univ, Dept Comp Sci, Faisalabad 37610, Pakistan
来源
CURRENT SCIENCE | 2019年 / 117卷 / 06期
关键词
Cosine similarity measure; document clustering; F-measure; hierarchical agglomerative clustering; preprocessing; TF-IDF;
D O I
10.18520/cs/v117/i6/1045-1053
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Clustering algorithms are used to generate clusters of elements having similar characteristics. Among the different groups of clustering algorithms, agglomerative algorithm is widely used in the document clustering domain. This study aimed to examine the effectiveness of agglomerative clustering algorithm in document clustering by enhancing its efficiency and evaluating it through implementation. The resulting values, precision = 0.8571, recall = 0.8571 and F-measure = 0.857076 indicate the highest level of accuracy and efficiency compared to existing algorithm.
引用
收藏
页码:1045 / 1053
页数:9
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