Fraction-Score: A New Support Measure for Co-location Pattern Mining

PhD Thesis Proposal Defence

Title: "Fraction-Score: A New Support Measure for Co-location Pattern 


Mr. Kai Ho CHAN


Co-location patterns are well-established on spatial objects with 
categorical labels, which capture the phenomenon that objects with certain 
labels are often located in close geographic proximity. Similar to 
frequent itemsets, co-location patterns are defined based on a support 
measure which quantifies the popularity (or prevalence) of a pattern 
candidate (a label set). Quite a few support measures exist for defining 
co-location patterns and they share an idea of counting the number of 
instances of a given label set C as its support, where an instance of C is 
an object set whose objects carry all the labels in C and are located 
close to one another. Unfortunately, these measures suffer from various 
weaknesses, e.g., some fail to capture all possible instances while some 
others overlook the cases when multiple instances overlap. In this thesis, 
we propose a new measure called Fraction-Score whose idea is to count 
instances fractionally if they overlap. Compared to existing measures, 
Fraction-Score not only captures all possible instances, but also handles 
the cases where instances overlap appropriately (so that the supports 
defined are more meaningful and consistent with the desirable 
anti-monotonicity property). To solve the co-location pattern mining 
problem based on Fraction-Score, we develop efficient algorithms which are 
significantly faster than a baseline that adapts the state-of-the-art. We 
conduct extensive experiments using both real and synthetic datasets, 
which verified the superiority of Fraction-Score and also the efficiency 
of our developed algorithms.

Date:			Wednesday, 19 December 2018

Time:                  	4:00pm - 6:00pm

Venue:                  Room 2131C
                         (lift 19)

Committee Members:	Dr. Raymond Wong (Supervisor)
 			Prof. Dimitris Papadias (Chairperson)
 			Prof. Gary Chan
 			Prof. Dit-Yan Yeung

**** ALL are Welcome ****