DocumentCode :
2547762
Title :
Finding criminal suspects by improving the accuracy of similarity measurement
Author :
Zhou, Xianshan ; Yu, Guangzhu
Author_Institution :
Coll. of Comput. Sci., Yangtzeu Univ., Jingzhou, China
fYear :
2012
fDate :
29-31 May 2012
Firstpage :
1145
Lastpage :
1149
Abstract :
Clustering technique was introduced to the field of crime data analysis for finding suspects, but traditional clustering methods used in existing application systems do not provide enough accuracy to meet the high requirements of police work. To solve the problem of low accuracy, we propose a hybrid similarity measurement, i.e., Segmented Multiple-Metric Similarity Measurement (SMMSM). In our method, compensation relationships among attributes are analyzed, attributes are grouped into multiple subsets, different measurements can be used in the meantime to measure the similarity of two objects, and the principles of classifying attributes are discussed. Experiment results show that our method has higher performance on accuracy and efficiency than traditional clustering methods.
Keywords :
criminal law; data analysis; government data processing; pattern classification; public administration; attribute classification; clustering technique; compensation relationship; crime data analysis; criminal suspect; hybrid similarity measurement; segmented multiple-metric similarity measurement; Accuracy; Cities and towns; Equations; Image color analysis; Mathematical model; Measurement; Vectors; clustering; compensation relationship; hybrid similarity measurement; multiple-metric; segmented;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on
Conference_Location :
Sichuan
Print_ISBN :
978-1-4673-0025-4
Type :
conf
DOI :
10.1109/FSKD.2012.6234080
Filename :
6234080
Link To Document :
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