Butt, Umair Muneer
(2024)
Clustering Ensemble And Hybrid Of
Deep Learning For Spatio-Temporal
Crime Predictions.
PhD thesis, Universiti Sains Malaysia.
Abstract
The increase in the urban population poses challenges in managing services and
safety from criminal activities. The concerned stakeholders intend to predict the time,
location, number, and types of crimes to take suitable preventive measures. Accurate
identification and prediction of crime hotspots can significantly benefit the concerned
stakeholders in preventing crime by creating accurate threat visualizations and allocating
police resources efficiently. Several techniques have been proposed for crime
prediction, but they are limited in accuracy and predicting crime according to crime
type on an hourly, monthly, and seasonal basis. Crime hotspot detection approaches
are primarily sensitive to initial parameter selection and finding clusters of varying
shapes and densities. Similarly, existing Crime prediction approaches are limited in
capturing non-stationary data and long-term dependencies by focusing on crime types.
Thus, the crime detection and prediction mechanisms need improvement in the number
of crimes, crime span, accuracy, and dense crime region and prediction. The core
objective of this study is twofold. First, it proposes a crime hotspot detection model to
improve accuracy using Hierarchical Density-Based Spatial Clustering of Applications
with Noise (HDBSCAN) and its clustering ensemble to capture varying shapes and
densities clusters and improve accuracy. HDBSCAN is used with varying parameter
initialization in the generation mechanism under the cluster ensemble paradigm. Moreover,
six different distance measures are used to ensure diversity. In addition, an evaluation
function is proposed parameterized by silhouette score to select the stable clustering
among a pool of clustering solutions to ensure quality.
Actions (login required)
 |
View Item |
