Density-Based Spatial Anomalous Window Discovery

Prerna Mohod and Vandana P. Janeja
Additional contact information
Prerna Mohod: University of Maryland, Baltimore County, USA
Vandana P. Janeja: University of Maryland, Baltimore County, USA

International Journal of Data Warehousing and Mining (IJDWM), 2022, vol. 18, issue 1, 1-23

Abstract: The focus of this paper is to identify anomalous spatial windows using clustering-based methods. Spatial Anomalous windows are the contiguous groupings of spatial nodes which are unusual with respect to the rest of the data. Many scan statistics based approaches have been proposed for the identification of spatial anomalous windows. To identify similarly behaving groups of points, clustering techniques have been proposed. There are parallels between both types of approaches but these approaches have not been used interchangeably. Thus, the focus of our work is to bridge this gap and identify anomalous spatial windows using clustering based methods. Specifically, we use the circular scan statistic based approach and DBSCAN- Density based Spatial Clustering of Applications with Noise, to bridge the gap between clustering and scan statistics based approach. We present experimental results in US crime data Our results show that our approach is effective in identifying spatial anomalous windows and performs equal or better than existing techniques and does better than pure clustering.

Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDWM.299015 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jdwm00:v:18:y:2022:i:1:p:1-23

Access Statistics for this article

International Journal of Data Warehousing and Mining (IJDWM) is currently edited by Eric Pardede

More articles in International Journal of Data Warehousing and Mining (IJDWM) from IGI Global
Bibliographic data for series maintained by Journal Editor ().