• Publications
  • Influence
gSpan: graph-based substructure pattern mining
  • X. Yan, Jiawei Han
  • Mathematics, Computer Science
  • IEEE International Conference on Data Mining…
  • 9 December 2002
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. Expand
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PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks
We introduce a meta path-based similarity framework for objects that are defined among the same type of objects in heterogeneous networks. Expand
  • 988
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CloSpan: Mining Closed Sequential Patterns in Large Datasets
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Frequent pattern mining: current status and future directions
Frequent pattern mining has been a focused theme in data mining research for over a decade. Expand
  • 1,333
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Graph indexing: a frequent structure-based approach
We investigate the issues of indexing graphs and propose a novel solution by applying a graph mining technique. Expand
  • 635
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Mining Frequent Patterns in Data Streams at Multiple Time Granularities
In this paper, we propose computing and maintaining all the frequent patterns (which is usually more stable and smaller than the streaming data) and dynamically updating them with the incoming data streams. Expand
  • 575
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CloseGraph: mining closed frequent graph patterns
A closed graph pattern mining algorithm, CloseGraph, is developed by exploring several interesting pruning methods to mine closed frequent graph patterns. Expand
  • 700
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SOBER: statistical model-based bug localization
We propose a new statistical model-based approach, called SOBER, which localizes software bugs without any prior knowledge of program semantics, which can help programmers locate 68 out of 130 bugs in the Siemens suite when programmers are expected to examine no more than 10% of the code. Expand
  • 406
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Statistical Debugging: A Hypothesis Testing-Based Approach
We propose a new statistical method, called SOBER, which automatically localizes software faults without any prior knowledge of the program semantics. Expand
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Discriminative Frequent Pattern Analysis for Effective Classification
We investigate the framework of frequent pattern-based classification, where a classification model is built in the feature space of single features as well as frequent patterns. Expand
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