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This paper proposes a novel approach named AGM to e-ciently mine the association rules among the frequently appearing sub-structures in a given graph data set. A graph transaction is represented by an adjacency matrix, and the frequent patterns appearing in the matrices are mined through the extended algorithm of the basket analysis. Its performance has… (More)

A new kernel function between two labeled graphs is presented. Feature vectors are defined as the counts of label paths produced by random walks on graphs. The kernel computation finally boils down to obtaining the stationary state of a discrete-time linear system , thus is efficiently performed by solving simultaneous linear equations. Our kernel is based… (More)

This chapter discusses the construction of kernel functions between labeled graphs. We provide a unified account of a family of kernels called label sequence kernels that are defined via label sequences generated by graph traversal. For cyclic graphs, dynamic programming techniques cannot simply be applied, because the kernel is based on an infinite… (More)

Basket Analysis, which is a standard method for data mining, derives frequent itemsets from database. However, its mining ability is limited to transaction data consisting of items. In reality, there are many applications where data are described in a more structural way, e.g. chemical compounds and Web browsing history. There are a few approaches that can… (More)

There are increasingly visible demands for structured/ unstructured information integration and advanced analytics. However, conventional database technology has not been able to present a robust and practical implementation of a truly integrated architecture for such purposes. After working on several industrial applications (in particular, in the… (More)

The Apriori-based graph mining method is an extension of the Apriori algorithm for association rule mining. It constructs a lattice of graph nodes, in which a node at the k-th level of the lattice has k vertices and the number of supporting instances exceeds a user-specified minimum support. The method can devise a rule " IF subgraph G a is in transaction… (More)

The mining of a complete set of frequent subgraphs from labeled graph data has been studied extensively. Furthermore, much attention has recently been paid to frequent pattern mining from graph sequences (dynamic graphs or evolving graphs). In this paper, we define a novel class of subgraph subsequence called an " induced subgraph subsequence " to enable… (More)