Discovering temporal patterns for interval-based events.

Kam, Po-shan.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 89-97).Abstracts in English and Chinese.Abstract --- p.iAcknowledgements --- p.iiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Data Mining --- p.1Chapter 1.2 --- Temporal Data Management --- p.2Chapter 1.3 --- Temporal reasoning and temporal semantics --- p.3Chapter 1.4 --- Temporal Data Mining --- p.5Chapter 1.5 --- Motivation --- p.6Chapter 1.6 --- Approach --- p.7Chapter 1.6.1 --- Focus and Objectives --- p.8Chapter 1.6.2 --- Experimental Setup --- p.8Chapter 1.7 --- Outline and contributions --- p.9Chapter 2 --- Relevant Work --- p.10Chapter 2.1 --- Data Mining --- p.10Chapter 2.1.1 --- Association Rules --- p.13Chapter 2.1.2 --- Classification --- p.15Chapter 2.1.3 --- Clustering --- p.16Chapter 2.2 --- Sequential Pattern --- p.17Chapter 2.2.1 --- Frequent Patterns --- p.18Chapter 2.2.2 --- Interesting Patterns --- p.20Chapter 2.2.3 --- Granularity --- p.21Chapter 2.3 --- Temporal Database --- p.21Chapter 2.4 --- Temporal Reasoning --- p.23Chapter 2.4.1 --- Natural Language Expression --- p.24Chapter 2.4.2 --- Temporal Logic Approach --- p.25Chapter 2.5 --- Temporal Data Mining --- p.25Chapter 2.5.1 --- Framework --- p.25Chapter 2.5.2 --- Temporal Association Rules --- p.26Chapter 2.5.3 --- Attribute-Oriented Induction --- p.27Chapter 2.5.4 --- Time Series Analysis --- p.27Chapter 3 --- Discovering Temporal Patterns for interval-based events --- p.29Chapter 3.1 --- Temporal Database --- p.29Chapter 3.2 --- Allen's Taxonomy of Temporal Relationships --- p.31Chapter 3.3 --- "Mining Temporal Pattern, AppSeq and LinkSeq" --- p.33Chapter 3.3.1 --- A1 and A2 temporal pattern --- p.33Chapter 3.3.2 --- "Second Temporal Pattern, LinkSeq" --- p.34Chapter 3.4 --- Overview of the Framework --- p.35Chapter 3.4.1 --- "Mining Temporal Pattern I, AppSeq" --- p.36Chapter 3.4.2 --- "Mining Temporal Pattern II, LinkSeq" --- p.36Chapter 3.5 --- Summary --- p.37Chapter 4 --- "Mining Temporal Pattern I, AppSeq" --- p.38Chapter 4.1 --- Problem Statement --- p.38Chapter 4.2 --- Mining A1 Temporal Patterns --- p.40Chapter 4.2.1 --- Candidate Generation --- p.43Chapter 4.2.2 --- Large k-Items Generation --- p.46Chapter 4.3 --- Mining A2 Temporal Patterns --- p.48Chapter 4.3.1 --- Candidate Generation: --- p.49Chapter 4.3.2 --- Generating Large 2k-Items: --- p.51Chapter 4.4 --- Modified AppOne and AppTwo --- p.51Chapter 4.5 --- Performance Study --- p.53Chapter 4.5.1 --- Experimental Setup --- p.53Chapter 4.5.2 --- Experimental Results --- p.54Chapter 4.5.3 --- Medical Data --- p.58Chapter 4.6 --- Summary --- p.60Chapter 5 --- "Mining Temporal Pattern II, LinkSeq" --- p.62Chapter 5.1 --- Problem Statement --- p.62Chapter 5.2 --- "First Method for Mining LinkSeq, LinkApp" --- p.63Chapter 5.3 --- "Second Method for Mining LinkSeq, LinkTwo" --- p.64Chapter 5.4 --- "Alternative Method for Mining LinkSeq, LinkTree" --- p.65Chapter 5.4.1 --- Sequence Tree: Design --- p.65Chapter 5.4.2 --- Construction of seq-tree --- p.69Chapter 5.4.3 --- Mining LinkSeq using seq-tree --- p.76Chapter 5.5 --- Performance Study --- p.82Chapter 5.6 --- Discussions --- p.85Chapter 5.7 --- Summary --- p.85Chapter 6 --- Conclusion and Future Work --- p.87Chapter 6.1 --- Conclusion --- p.87Chapter 6.2 --- Future Work --- p.88Bibliography --- p.9

Evolving temporal association rules with genetic algorithms

A novel framework for mining temporal association rules by discovering itemsets with a genetic algorithm is introduced. Metaheuristics have been applied to association rule mining, we show the efficacy of extending this to another variant - temporal association rule mining. Our framework is an enhancement to existing temporal association rule mining methods as it employs a genetic algorithm to simultaneously search the rule space and temporal space. A methodology for validating the ability of the proposed framework isolates target temporal itemsets in synthetic datasets. The Iterative Rule Learning method successfully discovers these targets in datasets with varying levels of difficulty