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DISCOVERING ASSOCIATION PATTERNS IN LARGE SPATIO-TEMPORAL

DATABASES

Data mining is concerned with the discovery of hidden patterns in large databases. Among the

different types of patterns that can be discovered, "association" patterns are the most important.

This is because the discovery of association patterns can lead more easily to the discovery of other

patterns for such data mining tasks as classification, clustering or prediction. Given a set of data

collected over a certain time period and over a number of different locations, existing data mining

approaches do not provide suitable tools to allow association patterns in such a data set to be easily

discovered.

The objective of this study is therefore to develop new approaches so that patterns that changes

from time-period to time-period and from location to location can be discovered. Making use of

techniques in meta-mining, probability and statistics, and such techniques as machine learning and

fuzzy logic, our objective is to develop data mining techniques capable of discovering such

patterns in spatio-temporal databases. Over the past few years, a considerable number of studies

have been made on market basket analysis. Market basket analysis is a useful method for

discovering customer purchasing patterns by extracting association from stores' transaction

database. In many business of today, customer transactions can be made in many different

geographical locations round the clock, especially after e-business and online shops have become

prevalent.

The traditional methods that consider only the association rules of an individual location or all

locations as a whole are not suitable for such a multi-location environment. Understanding and

adapting to changes of customer behavior from time to time and from place to place is an

important aspect for a company having transactions collected from multi-locations, for example

those running business-to-customer (B2C) business, to survive in continuously changing

environment. If applied to B2C business, the methodology developed in this study allow

companies to detect changes of customer behavior automatically from customer profiles, in which

customers may come from different places over the world, and sales data may be inputted at

different time snapshots.

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There are three main contributions in the thesis. Firstly, we design a novel and efficient algorithm

for mining spatio-temporal association rules which have multi-level time and location

granularities, in spatio-temporal databases. From the perspective of business strategists, the

discovered rules also must be readily interpreted for easy reading and further usage, in order to be

useful. However, different executive personnel will require different interpretation of the rules in

different usage scenarios. And under different granularities of time-and-place, the knowledge will

be different.

The goal of our work is to satisfy such dynamic needs. In this study, we develop an algorithm that

can find association rules under different granularities of time-and-place to satisfy the different

demands of different decision makers. Unlike Apriori-like approaches, our method scans the

database at most twice. By avoiding multiple scans over the target database, our method can reduce

the runtime in scanning database. Secondly, we use membership functions to construct fuzzy

calendar-map patterns which represent asynchronous time periods and locations. With the use of

fuzzy calendar-map patterns, we can discover fuzzy spatio-temporal association rules which are

defined as association rules occur in asynchronous time periods and/or locations. Thirdly, we

propose to mine a set of rules from the discovered collection of spatio-temporal rule sets.

These meta-rules, rules about rules, represent the kind of knowledge that few existing data mining

algorithms have been developed to mine for. In this study, we define problems in discovering the

underlying regularities, differences, and changes hidden in spatio-temporal rule sets and propose a

new approach, meta-mining spatio-temporal patterns, which mines previous spatio-temporal

association rule mining results to discover these underlying regularities, differences, and changes.

Experimental results have shown that our methods are more efficient than others, and we can find

fuzzy spatio-temporal association rules satisfactorily and so as meta-rules among the set of rules

discovered.