Discovering temporal patterns for interval-based events.

• Other Authors: Kam, Po-shan.
• Format: Others
• Language: English; Chinese
• Published: 2000
• Subjects: Data mining; Temporal databases
• Online Access: http://library.cuhk.edu.hk/record=b5890407; http://repository.lib.cuhk.edu.hk/en/item/cuhk-323084

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