Title :
Research on Application of Cluster Analysis in Fire Fighting Domain
Author_Institution :
Dept. of Fire Eng., Chinese People´s Armed Police Force Acad., Langfang, China
Abstract :
The paper adopts k-means cluster analysis in SSPS to mine 3063 collected data samples of fire fighting receive-disposal alarm in a certain city from 2009 to 2012, selects four describable attributes of these data as cluster variables, then verifies the validity of cluster variables and analyzes the mining results. The results show that, there exists certain features in clusters formed by data of fire receive-disposal alarm, which has guiding significance for fire-fighting and rescue work, meanwhile, makes primary exploration into application of data mining in analyzing data of fire fighting receive-disposal alarm.
Keywords :
alarm systems; data mining; emergency services; feature selection; fires; pattern clustering; statistical analysis; cluster features; cluster variables; data mining; fire fighting receive-disposal alarm; k-means cluster analysis; Algorithm design and analysis; Cities and towns; Clustering algorithms; Data mining; Fires; Linear programming; Vehicles; ANOVA; data mining; fire fighting receive-disposal alarm; k-means clustering;
Conference_Titel :
Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2014 Sixth International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4799-4956-4
DOI :
10.1109/IHMSC.2014.143
