Cascading Spatio-temporal Pattern Discovery: A Summary of Results

Given a collection of Boolean spatio-temporal(ST) event types, the cascading spatio-temporal pattern (CSTP) discovery process finds partially ordered subsets of event-types whose instances are located together and occur in stages. For example, analysis of crime datasets may reveal frequent occurrence of misdemeanors and drunk driving after bar closings on weekends and after large gatherings such as football games. Discovering CSTPs from ST datasets is important for application domains such as public safety (e.g. crime attractors and generators) and natural disaster planning(e.g. hurricanes). However, CSTP discovery is challenging for several reasons, including both the lack of computationally efficient, statistically meaningful metrics to quantify interestingness, and the large cardinality of candidate pattern sets that are exponential in the number of event types. Existing literature for ST data mining focuses on mining totally ordered sequences or unordered subsets. In contrast, thi...

• Type: Conference
• Year: 2010
• Where: SDM
• Authors: Pradeep Mohan, Shashi Shekhar, James A. Shine, James P. Rogers
• Keywords: Cascading Spatio-temporal Pattern, Data Mining, Event Types, Partially Ordered, SDM 2010