A Combined System for Regionalization in Spatial Data Mining Based on Fuzzy C-Means Algorithm with Gravitational Search Algorithm

被引：2
|
作者：
Sheshasaayee, Ananthi [1 ]
Sridevi, D. [2 ]
机构：
[1] Quaid E Millath Goverment Coll Women Autonomous, Chennai, Tamil Nadu, India
[2] Sri Chandrasekharendra Saraswathi Viswa Maha Vidy, Dept Comp Sci, Kanchipuram, Tamil Nadu, India
来源：
PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON FRONTIERS IN INTELLIGENT COMPUTING: THEORY AND APPLICATIONS, (FICTA 2016), VOL 2 | 2017年 / 516卷
关键词：
Spatial data mining; Clustering; Fuzzy c-means; Gravitational search algorithm; Regionalization; Metrics;
D O I：
10.1007/978-981-10-3156-4_54
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The proposed new hybrid approach for data clustering is achieved by initially exploiting spatial fuzzy c-means for clustering the vertex into homogeneous regions. Further to improve the fuzzy c-means with its achievement in segmentation, we make use of gravitational search algorithm which is inspired by Newton's rule of gravity. In this paper, a modified modularity measure to optimize the cluster is presented. The technique is evaluated under standard metrics of accuracy, sensitivity, specificity, Map, RMSE and MAD. From the results, we can infer that the proposed technique has obtained good results.
引用
收藏
页码：517 / 524
页数：8
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