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  4. Change points detection in crime-related time series: An on-line fuzzy approach based on a shape space representation
 
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Change points detection in crime-related time series: An on-line fuzzy approach based on a shape space representation

Auteur(s)
Albertetti, Fabrizio 
Institut du management de l'information 
Grossrieder, Lionel
Ribaux, Olivier
Stoffel, Kilian 
Institut du management de l'information 
Date de parution
2015-12-17
In
Applied Soft Computing
No
Volume
De la page
1
A la page
1
Revu par les pairs
1
Mots-clés
  • Change points detection
  • Qualitative description of data
  • Time series analysis
  • Fuzzy logic
  • Crime analysis
  • Change points detecti...

  • Qualitative descripti...

  • Time series analysis

  • Fuzzy logic

  • Crime analysis

Résumé
The extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.
Lié au projet
An Intelligent Process-driven Knowledge Extraction Framework for Crime Analysis 
Identifiants
https://libra.unine.ch/handle/123456789/24279
_
10.1016/j.asoc.2015.12.004
Type de publication
journal article
Dossier(s) à télécharger
 main article: 1-s2.0-S1568494615007838-main.pdf (2.79 MB)
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