Akihiro Inokuchi

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This paper proposes a novel approach named AGM to e ciently mine the association rules among the frequently appearing substructures 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 been(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)
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)
The problem of mining frequent itemsets in transactional data has been studied frequently and has yielded several algorithms that can find the itemsets within a limited amount of time. Some of them can derive "generalized" frequent itemsets consisting of items at any level of a taxonomy (Srikant and Agrawal, 1995). Several approaches have been proposed to(More)
Biomedical text plays a fundamental role in knowledge discovery in life science, in both basic research (in the field of bioinformatics) and in industry sectors devoted to improving medical practice, drug development, and health care (such as medical informatics, clinical genomics, and other sectors). Several groups in the IBM Research Division are(More)
In recent years, the mining of a complete set of frequent subgraphs from labeled graph data has been extensively studied.However, to our best knowledge, almost no methods have been proposed to find frequent subsequences of graphs from a set of graph sequences. In this paper, we define a novel class of graph subsequences by introducing axiomatic rules of(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)
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 Ga is in transaction G,(More)