Graphic and numerical methods to access navigation in hypertext
International Journal of Human-Computer Studies
Using Site Semantics to Analyze, Visualize, and Support Navigation
Data Mining and Knowledge Discovery
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Analysis of navigation behaviour in web sites integrating multiple information systems
The VLDB Journal — The International Journal on Very Large Data Bases
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs
ADL '98 Proceedings of the Advances in Digital Libraries Conference
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
SEWeP: using site semantics and a taxonomy to enhance the Web personalization process
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A quickstart in frequent structure mining can make a difference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Generalized Substructures from a Set of Labeled Graphs
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Mining closed relational graphs with connectivity constraints
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Discovering frequent topological structures from graph datasets
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A maximum entropy web recommendation system: combining collaborative and content features
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Large scale mining of molecular fragments with wildcards
Intelligent Data Analysis
AWIC'03 Proceedings of the 1st international Atlantic web intelligence conference on Advances in web intelligence
A quantitative comparison of the subgraph miners mofa, gspan, FFSM, and gaston
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Integrating web conceptual modeling and web usage mining
WebKDD'04 Proceedings of the 6th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
From Web to Social Web: Discovering and Deploying User and Content Profiles
Independent informative subgraph mining for graph information retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
Guest editorial: special issue on a decade of mining the Web
Data Mining and Knowledge Discovery
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The search for frequent subgraphs is becoming increasingly important in many application areas including Web mining and bioinformatics. Any use of graph structures in mining, however, should also take into account that it is essential to integrate background knowledge into the analysis, and that patterns must be studied at different levels of abstraction. To capture these needs, we propose to use taxonomies in mining and to extend frequency / support measures by the notion of context-induced interestingness. The AP-IP mining problem is to find all frequent abstract patterns and the individual patterns that constitute them and are therefore interesting in this context (even though they may be infrequent). The paper presents the fAP-IP algorithm that uses a taxonomy to search for the abstract and individual patterns, and that supports graph clustering to discover further structure in the individual patterns. Semantics are used as well as learned in this process. fAP-IP is implemented as an extension of Gaston (Nijssen & Kok, 2004), and it is complemented by the AP-IP visualization tool that allows the user to navigate through detail-and-context views of taxonomy context, pattern context, and transaction context. A case study of a real-life Web site shows the advantages of the proposed solutions. ACM categories and subject descriptors and keywords: H.2.8 [Database Management]: Database Applications—data mining; H.5.4 [Information Interfaces and Presentation]: Hypertext/Hypermedia —navigation, user issues; graph mining; Web mining; background knowledge and semantics in mining.