Constructing Decision Trees for Graph-Structured Data by Chunkingless Graph-Based Induction

A decision tree is an effective means of data classification from which one can obtain rules that are easy to understand. However, decision trees cannot be conventionally constructed for data which are not explicitly expressed with attribute-value pairs such as graph-structured data. We have proposed a novel algorithm, named Chunkingless Graph-Based Induction (Cl-GBI), for extracting typical patterns from graph-structured data. Cl-GBI is an improved version of Graph-Based Induction (GBI) which employs stepwise pair expansion (pairwise chunking) to extract typical patterns from graphstructured data, and can find overlapping patterns that cannot not be found by GBI. In this paper, we further propose an algorithm for constructing decision trees for graphstructured data using Cl-GBI. This decision tree construction algorithm, now called Decision Tree Chunkingless Graph-Based Induction (DT-ClGBI), can construct a decision tree from a graph-structured dataset while simultaneously constructing attributes useful for classification using Cl-GBI internally. Since patterns (subgraphs) extracted by Cl-GBI are considered as attributes of a graph, and their existence/non-existence are used as attribute values in DT-ClGBI, DT-ClGBI can be conceived as a tree generator equipped with feature construction capability. Experiments were conducted on both synthetic and real-world graph-structured datasets showing the usefulness and effectiveness of the algorithm.