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We investigate new approaches for frequent graph-based pattern mining in graph datasets and propose a novel algorithm called gSpan (graph-based Substructure pattern mining), which discovers frequent substructures without candidate generation. gSpan builds a new lexicographic order among graphs, and maps each graph to a unique minimum DFS code as its(More)
Frequent pattern mining has been a focused theme in data mining research for over a decade. Abundant literature has been dedicated to this research and tremendous progress has been made, ranging from efficient and scalable algorithms for frequent itemset mining in transaction databases to numerous research frontiers, such as sequential pattern mining,(More)
Although frequent-pattern mining has been widely studied and used, it is challenging to extend it to data streams. Compared with mining a static transaction data set, the streaming case has far more information to track and far greater complexity to manage. Infrequent items can become frequent later on and hence cannot be ignored. The storage structure need(More)
Graph has become increasingly important in modelling complicated structures and schemaless data such as proteins, chemical compounds, and XML documents. Given a <i>graph query</i>, it is desirable to retrieve graphs quickly from a large database via <i>graph-based indices.</i> In this paper, we investigate the issues of indexing graphs and propose a novel(More)
Similarity search is a primitive operation in database and Web search engines. With the advent of large-scale heterogeneous information networks that consist of multi-typed, interconnected objects, such as the bibliographic networks and social media networks, it is important to study similarity search in such networks. Intuitively, two objects are similar(More)
Recent research on pattern discovery has progressed form mining frequent <i>itemsets</i> and <i>sequences</i> to mining structured patterns including <i>trees, lattices</i>, and <i>graphs</i>. As a general data structure, <i>graph</i> can model complicated relations among data with wide applications in bioinformatics, Web exploration, and etc. However,(More)
Automated localization of software bugs is one of the essential issues in debugging aids. Previous studies indicated that the evaluation history of program predicates may disclose important clues about underlying bugs. In this paper, we propose a new statistical model-based approach, called SOBER, which localizes software bugs without any prior knowledge of(More)
Manual debugging is tedious, as well as costly. The high cost has motivated the development of fault localization techniques, which help developers search for fault locations. In this paper, we propose a new statistical method, called SOBER, which automatically localizes software faults without any prior knowledge of the program semantics. Unlike existing(More)
The application of frequent patterns in classification appeared in sporadic studies and achieved initial success in the classification of relational data, text documents and graphs. In this paper, we conduct a systematic exploration of frequent pattern-based classification, and provide solid reasons supporting this methodology. It was well known that(More)