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Title: Temporal data mining : algorithms, language and system for temporal association rules.
Author: Chen, Xiaodong.
Awarding Body: Manchester Metropolitan University
Current Institution: Manchester Metropolitan University
Date of Award: 1999
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Studies on data mining are being pursued in many different research areas, such as Machine Learning, Statistics, and Databases. The work presented in this thesis is based on the database perspective of data mining. The main focuses are on the temporal aspects of data mining problems, especially association rule discovery, and issues on the integration of data mining and database systems. Firstly, a theoretical framework for temporal data mining is proposed in this thesis. Within this framework, not only potential patterns but also temporal features associated with the patterns are expected to be discovered. Calendar time expressions are suggested to represent temporal features and the minimum frequency of patterns is introduced as a new threshold in the model of temporal data mining. The framework also emphasises the necessary components to support temporal data mining tasks. As a specialisation of the proposed framework, the problem of mining temporal association rules is investigated. The methodology adopted in this thesis is eventually discovering potential temporal rules by alternatively using special search techniques for various restricted problems in an interactive and iterative process. Three forms of interesting mining tasks for temporal association rules with certain constraints are identified. These tasks are the discovery of valid time periods of association rules, the discovery of periodicities of association rules, and the discovery of association rules with temporal features. The search techniques and algorithms for those individual tasks are developed and presented in this thesis. Finally, an integrated query and mining system (IQMS) is presented in this thesis, covering the description of an interactive query and mining interface (IQMI) supplied by the IQMS system, the presentation of an SQL-like temporal mining language (TML) with the ability to express various data mining tasks for temporal association rules, and the suggestion of an IQMI-based interactive data mining process. The implementation of this system demonstrates an alternative approach for the integration of the DBMS and data mining functions.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Knowledge discovery; Temporal pattern and data Artificial intelligence Computer software