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Mining Discriminative Patterns from Graph Structured Data with Constrained Search
| Citations: | 3 - 0 self |
BibTeX
@MISC{Takabayashi_miningdiscriminative,
author = {Kiyoto Takabayashi and Phu Chien Nguyen and Kouzou Ohara and Hiroshi Motoda and Takashi Washio},
title = {Mining Discriminative Patterns from Graph Structured Data with Constrained Search},
year = {}
}
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Abstract
Abstract. A graph mining method, Chunkingless Graph-Based Induction (Cl-GBI), finds typical patterns that appear in graph structured data by the operation called chunkingless pairwise expansion which generates pseudo-nodes from selected pairs of nodes in the data. Cl-GBI enables to extract overlapping subgraphs, while it requires more time and space complexities. Thus, it happens that Cl-GBI cannot extract patterns that need be large enough to describe characteristics of data within a limited time and a given computational resource. In such a case, extracted patterns may not be so much of interest for domain experts. To mine more discriminative patterns which cannot be extracted by the current Cl-GBI, we introduce a search algorithm guided by domain knowledge or interests of domain experts. We further experimentally show that the proposed method can efficiently extract more discriminative patterns using both synthetic and real world datasets. 1
Keyphrases
discriminative pattern constrained search graph structured data domain expert domain knowledge chunkingless graph-based induction computational resource cl-gbi cannot extract real world datasets limited time cl-gbi enables graph mining method chunkingless pairwise expansion current cl-gbi search algorithm space complexity typical pattern
