Scalable Mining and Link Analysis Across Multiple Database Relations

Because of the complexity of multi-relational data, efficiency and scalability are two major concerns in multi-relational data mining. In this thesis we propose scalable and accurate approaches for each data mining task studied. In order to achieve high efficiency and scalability, the approaches utilize novel techniques for virtually joining different relations, single-scan algorithms, and multi-resolutional data structures to dramatically reduce computational costs. Our experiments show that our approaches are highly efficient and scalable, and also achieve high accuracies in multi-relational data mining.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Yin, Xiaoxin

Contributors dc:contributor

• Han, Jiawei

• Computer Science

Language dc:language

eng

Identifier

(MiAaPQ)AAI3270062

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/81770