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Graph Data Mining Techniques

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Kasun Gajasinghe
Kasun Gajasinghe
Technology
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Sriskandarajah Suhothayan Kasun Gajasinghe Isuru Loku Narangoda Subash Chaturanga
Outline ,[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object]
Graph based data mining ,[object Object],[object Object]
Approaches ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Applications ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Basic Principles ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Basic Principles ,[object Object],[object Object],[object Object],[object Object],[object Object]
Solution Approaches direct Categorization Completeness complete search heuristic search Subgraph isomorphism matching problem Indirect (solves the subgraph similarity problem)
Solution Approaches ,[object Object],[object Object],[object Object],[object Object],[object Object]
Greedy search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Inductive logic programming (ILP) ,[object Object],[object Object]
Inductive logic programming (ILP) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Inductive database ,[object Object],[object Object],[object Object]
Complete level-wise search ,[object Object],[object Object],[object Object],[object Object]
Support Vector Machine (SVM) ,[object Object],[object Object],[object Object]
Categorization ,[object Object],[object Object],[object Object],[object Object],[object Object]
Greedy Search Based Approaches ,[object Object],[object Object],[object Object],[object Object]
Graph Based Induction (GBI) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
SUBDUE ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Mathematical Approaches ,[object Object],[object Object],[object Object],[object Object],[object Object]
Apriori-based Approach ,[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Pattern growth mathod
GRAPH DATASET FREQUENT PATTERNS (MIN SUPPORT IS 2) (A) (B) (C) (1) (2)
Another three approaches to mine graph based data. ,[object Object],[object Object],[object Object]
ILP approach. ILP systems constructs predictive model for a given data set by searching large space of candidate hypothesis. ,[object Object],[object Object],[object Object]
Inductive DB approach. Databases which are capable of handling patterns within data. Quite different from from typical data bases. Uses interactive querying process to mine data in these data bases. ,[object Object],[object Object]
Kernel Function based approach This “kernel” function basically defines similarity between two graphs The paper consists of two efforts done based on this approach, which classifies the graphs in to binary classes by SVM (Support Vector - Machine).

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Graph Data Mining Techniques

  • 1. Sriskandarajah Suhothayan Kasun Gajasinghe Isuru Loku Narangoda Subash Chaturanga
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  • 9. Solution Approaches direct Categorization Completeness complete search heuristic search Subgraph isomorphism matching problem Indirect (solves the subgraph similarity problem)
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  • 27. GRAPH DATASET FREQUENT PATTERNS (MIN SUPPORT IS 2) (A) (B) (C) (1) (2)
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  • 31. Kernel Function based approach This “kernel” function basically defines similarity between two graphs The paper consists of two efforts done based on this approach, which classifies the graphs in to binary classes by SVM (Support Vector - Machine).