A Historical Disaster Classification Method Based on Ant Colony Clustering

doi:10.3724/SP.J.1087.2012.01030

Journal of Computer Applications ›› 2012, Vol. 32 ›› Issue (04): 1030-1032.DOI: 10.3724/SP.J.1087.2012.01030

• Artificial intelligence • Previous Articles Next Articles

A Historical Disaster Classification Method Based on Ant Colony Clustering

JIA Zhi-juan¹,HU Ming-sheng²,LIU Si²

1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan Hubei 430070, China
2. Network Center, Zhengzhou Normal University, Zhengzhou Henan 450044, China

Received:2011-10-17 Revised:2011-12-07 Online:2012-04-20 Published:2012-04-01
Contact: LIU Si
Supported by:
;the Natural Science Research Projects of Education Department of Henan Province

基于蚁群聚类的历史灾害分级方法

贾志娟¹,胡明生²,刘思²

1. 武汉理工大学计算机科学与技术学院，武汉 430070
2. 郑州师范学院网络中心，郑州 450044

通讯作者: 刘思
作者简介:贾志娟(1973-)，女，河南郑州人，博士研究生，主要研究方向：人工智能、数据挖掘；胡明生(1973-)，男，河南新县人，副教授，博士，主要研究方向：智能控制与决策支持；刘思(1985-)，男，河南信阳人，硕士研究生，主要研究方向：人工智能。
基金资助:
国家自然科学基金资助项目;河南省基础与前沿技术研究项目;河南省教育厅自然科学研究计划项目

Abstract

Abstract: Aiming at the descriptiveness and parsimony problems of historical disaster records a historical disaster classification method which based on ant colony clustering is proposed in this paper. The disaster data are normalized by using gray relational analysis approach, and then the levels of historical disasters are divided by the results of ant colony automatic clustering, so as to avoid the arbitrary man-made interference. In comparison with other classification methods in performance, experimental results show that this method has high accuracy and practicality.

Key words: historical disasters, classification, clustering, ant colony algorithm, gray relational analysis

摘要： 针对历史灾害记录的描述性、简约性问题，提出一种基于蚁群聚类的历史灾害分级方法。利用灰色关联分析方法对灾害数据进行归一化处理后，再通过蚁群自动聚类的结果来划分历史灾害的等级，以避免人为的主观任意性干扰。通过与其他分级方法的性能对比，实验结果证明该方法具有较高的精确性和实用性。

关键词: 历史灾害, 分级, 聚类, 蚁群算法, 灰色关联分析

JIA Zhi-juan HU Ming-sheng LIU Si. A Historical Disaster Classification Method Based on Ant Colony Clustering[J]. Journal of Computer Applications, 2012, 32(04): 1030-1032.

贾志娟胡明生刘思. 基于蚁群聚类的历史灾害分级方法[J]. 计算机应用, 2012, 32(04): 1030-1032.

References

［1］防灾减灾空间信息建设亟待加强［EB/OL］. [2011-09-01]. http://news.xinhuanet.com/society/2011-05/12/c_121406775.htm. ［2］马细霞，李艳.基于主成分分析的农业旱灾等级区划研究［J］.郑州大学学报，2011,32(1):125-128. ［3］张星.自然灾害灾情的熵权综合评价模型［J］.自然灾害学报，2009,18(6):189-192. ［4］龚日朝，王爱平.基于信息融合的自然灾害等级评估方法研究［J］.中国安全科学学报，2010, 20(11):3-10. ［5］代博洋,李志强,李晓丽.基于物元理论的自然灾害损失等级划分方法［J］.灾害学，2009,24(1):1-5. ［6］高燕，陈云翔.基于免疫遗传算法的洪灾神经网络评估模型［J］.自然灾害学报，2006,15(1):110-114. ［7］徐海亮，陈亚宁.洪水灾害等级划分的模糊聚类分析［J］.干旱区地理，2000，23(4)：350-352. ［8］ ZHANG QIU-YU, YANG HUI-JUAN, WANG PENG, et al. Fuzzy clustering based on semantic body and its application in Chinese spam filtering［J］. JDCTA: International Journal of Digital Content Technology and its Applications, 2011, 5(4): 1-11. ［9］陈亚宁，杨思全.灰色聚类在洪水灾害等级划分中的应用［J］.干旱区地理，1999，22(3)：37-42. ［10］ WEN KUN-LI, YOU MEI-LI, LEE B-Y. Apply grey relationalgrade and rough set theory for the factor weighting analysis in liver function［J］. Journal of Convergence Information Technology, 2011, 6(9):420-428. ［11］李祚泳,汪嘉杨.基于蚁群算法的BP网络优化算法［J］.JOCA，2010,30(6):1513-1518. ［12］刘喜恩.用于连续空间寻优的一种蚁群算法［J］.JOCA，2009,29(10):2744-2747. ［13］ DORIGO M, STTZLE T. Ant colony optimization［M］.北京:清华大学出版社,2005. ［14］ CHIANG CHUAN-WEN, HUANG YU-QING, LU GUO-QIN, et al. Ant-inspired search techniques for solving the zero-one knapsack problem with multiple constraints［J］. International Journal of Advancements in Computing Technology, 2011, 3(4): 242-255. ［15］ HOMBERGER J, GEHRING H. An ant colony optimization-based negotiation approach for lot-sizing in supply chains［J］. International Journal of Information Processing and Management, 2011, 2(3): 86-99.

[1]	Shunyong LI, Shiyi LI, Rui XU, Xingwang ZHAO. Incomplete multi-view clustering algorithm based on self-attention fusion [J]. Journal of Computer Applications, 2024, 44(9): 2696-2703.
[2]	Yuxin HUANG, Jialong XU, Zhengtao YU, Shukai HOU, Jiaqi ZHOU. Unsupervised text sentiment transfer method based on generation prompt [J]. Journal of Computer Applications, 2024, 44(9): 2667-2673.
[3]	Chun SUN, Chunlong HU, Shucheng HUANG. Consistency preserving age estimation method by ensemble ranking [J]. Journal of Computer Applications, 2024, 44(8): 2381-2386.
[4]	Qiangkui LENG, Xuezi SUN, Xiangfu MENG. Oversampling method for imbalanced data based on sample potential and noise evolution [J]. Journal of Computer Applications, 2024, 44(8): 2466-2475.
[5]	Quanmei ZHANG, Runping HUANG, Fei TENG, Haibo ZHANG, Nan ZHOU. Automatic international classification of disease coding method incorporating heterogeneous information [J]. Journal of Computer Applications, 2024, 44(8): 2476-2482.
[6]	Dongwei WANG, Baichen LIU, Zhi HAN, Yanmei WANG, Yandong TANG. Deep network compression method based on low-rank decomposition and vector quantization [J]. Journal of Computer Applications, 2024, 44(7): 1987-1994.
[7]	Junchi GE, Weihua ZHAO. Distance weighted discriminant analysis based on robust principal component analysis for matrix data [J]. Journal of Computer Applications, 2024, 44(7): 2073-2079.
[8]	Qianhui LU, Yu ZHANG, Mengling WANG, Tingwei WU, Yuzhong SHAN. Classification model of nuclear power equipment quality text based on improved recurrent pooling network [J]. Journal of Computer Applications, 2024, 44(7): 2034-2040.
[9]	Qing WANG, Jieyu ZHAO, Xulun YE, Nongxiao WANG. Enhanced deep subspace clustering method with unified framework [J]. Journal of Computer Applications, 2024, 44(7): 1995-2003.
[10]	Xun YAO, Zhongzheng QIN, Jie YANG. Generative label adversarial text classification model [J]. Journal of Computer Applications, 2024, 44(6): 1781-1785.
[11]	Zixuan YUAN, Xiaoqing WENG, Ningzhen GE. Early classification model of multivariate time series based on orthogonal locality preserving projection and cost optimization [J]. Journal of Computer Applications, 2024, 44(6): 1832-1841.
[12]	Xu LI, Yulin HE, Laizhong CUI, Zhexue HUANG, Fournier‑Viger PHILIPPE. Distributed observation point classifier for big data with random sample partition [J]. Journal of Computer Applications, 2024, 44(6): 1727-1733.
[13]	Feiyu ZHAI, Handa MA. Hybrid classical-quantum classification model based on DenseNet [J]. Journal of Computer Applications, 2024, 44(6): 1905-1910.
[14]	Yao DONG, Yixue FU, Yongfeng DONG, Jin SHI, Chen CHEN. Survey of incomplete multi-view clustering [J]. Journal of Computer Applications, 2024, 44(6): 1673-1682.
[15]	Xiaoxia JIANG, Ruizhang HUANG, Ruina BAI, Lina REN, Yanping CHEN. Deep event clustering method based on event representation and contrastive learning [J]. Journal of Computer Applications, 2024, 44(6): 1734-1742.

A Historical Disaster Classification Method Based on Ant Colony Clustering

基于蚁群聚类的历史灾害分级方法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics