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Title: Object-oriented data mining
Author: Rawles, Simon Alan
ISNI:       0000 0001 3509 4324
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2007
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Attempts to overcome limitations in the attribute-value representation for machine learning has led to much interest in learning from structured data, concentrated in the research areas of inductive logic programming (ILP) and multi-relational data mining (MDRM). The expressivenessa nd encapsulationo f the object-oriented data model has led to its widespread adoption in software and database design. The considerable congruence between this model and individual-centred models in inductive logic programming presents new opportunities for mining object data specific to its domain. This thesis investigates the use of object-orientation in knowledge representation for multi-relational data mining. We propose a language for expressing object model metaknowledge and use it to extend the reasoning mechanisms of an object-oriented logic. A refinement operator is then defined and used for feature search in a object-oriented propositionalisation-based ILP classifier. An algorithm is proposed for reducing the large number of redundant features typical in propositionalisation. A data mining system based on the refinement operator is implemented and demonstrated on a real-world computational linguistics task and compared with a conventional ILP system. Keywords: Object orientation; data mining; inductive logic programming; propositionalisation; refinement operators; feature reduction
Supervisor: Flach, Peter Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available