Title :
Multi-Relational Data Mining Based on Higher-Order Inductive Logic Programming
Author_Institution :
Inst. of Comput. Technol., Chinese Acad. of Sci., Beijing, China
Abstract :
This paper presents a novel multi-relational data mining (MRDM) approach from a perspective of considering higher-order inductive logic programming to dealing with the representation formalism problems of existing multi-relational data mining approaches. In our approach, examples, background knowledge,hypotheses and target concepts are represented in Escher, a higher-order logic programming language.Escher can describe semantically complicated data and patterns, and explicitly supports a variety of data types, including graph. Moreover, our approach explores and exploits the techniques of HILP based on Escher to efficiently construct search space and proposal a novel methodology of MRDM.Furthermore, we present an architecture for efficiency and scalability of MRDM based on HILP. We believe that our approach based on higher-order inductive logic programming will has a key role to play in the growth of MRDM while several major call for algorithms that explicitly exploit the semantically complicated and topological substructures of data.
Keywords :
data mining; data structures; graph theory; inductive logic programming; relational databases; search problems; Escher; data pattern; data type; graph theory; higher-order inductive logic programming language; machine learning; multirelational data mining; representation formalism problem; search space; Computers; Data mining; Intelligent systems; Law; Legal factors; Logic programming; Machine learning; Relational databases; Scalability; Workstations; higher-order inductive logic programming; multi-relational data mining;
Conference_Titel :
Intelligent Systems, 2009. GCIS '09. WRI Global Congress on
Conference_Location :
Xiamen
Print_ISBN :
978-0-7695-3571-5
DOI :
10.1109/GCIS.2009.289