Knowledge discovery from tree databases using balanced optimal search
Description
This research is a step forward in discovering knowledge from databases of complex structure like tree or graph. Several data mining algorithms are developed based on a novel representation called Balanced Optimal Search for extracting implicit, unknown and potentially useful information like patterns, similarities and various relationships from tree data, which are also proved to be advantageous in analysing big data. This thesis focuses on analysing unordered tree data, which is robust to data inconsistency, irregularity and swift information changes, hence, in the era of big data it becomes a popular and widely used data model.
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| ID Code: | 92263 |
|---|---|
| Item Type: | QUT Thesis (PhD by Publication) |
| Supervisor: | Nayak, Richi & Karim, Azharul |
| Keywords: | Unordered Tree, Tree Representation, Frequent Subtree Mining, Tree Matching, Similarity Measure, Knowledge Discovery, Tree Database, Free Tree |
| Divisions: | Past > QUT Faculties & Divisions > Science & Engineering Faculty Past > Schools > School of Chemistry, Physics & Mechanical Engineering |
| Institution: | Queensland University of Technology |
| Deposited On: | 18 May 2016 05:14 |
| Last Modified: | 06 Sep 2017 14:42 |
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