Fast discovery of association rules
Advances in knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
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
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Towards generic pattern mining
ICFCA'05 Proceedings of the Third international conference on Formal Concept Analysis
Component-based decision trees for classification
Intelligent Data Analysis
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Frequent Pattern Mining (FPM) is a very powerful paradigm which encompasses an entire class of data mining tasks. The specific tasks encompassed by FPM include the mining of increasingly complex and informative patterns, in complex structured and unstructured relational datasets, such as: Itemsets or co-occurrences [1] (transactional, unordered data), Sequences [2,8] (temporal or positional data, as in text mining, bioinformatics), Tree patterns [9] (XML/semistructured data), and Graph patterns [4,5,6] (complex relational data, bioinformatics). Figure [1] shows examples of these different types of patterns; in a generic sense a pattern denotes links/relationships between several objects of interest. The objects are denoted as nodes, and the links as edges. Patterns can have multiple labels, denoting various attributes, on both the nodes and edges.