Comparison of interestingness functions for learning web usage patterns
Proceedings of the eleventh international conference on Information and knowledge management
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
The PSP Approach for Mining Sequential Patterns
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference 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
Efficiently Computing Frequent Tree-Like Topology Patterns in a Web Environment
TOOLS '99 Proceedings of the 31st International Conference on Technology of Object-Oriented Language and Systems
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Incremental mining of sequential patterns in large databases
Data & Knowledge Engineering
Mining Sequential Patterns Using Graph Search Techniques
COMPSAC '03 Proceedings of the 27th Annual International Conference on Computer Software and Applications
Itemset Trees for Targeted Association Querying
IEEE Transactions on Knowledge and Data Engineering
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Frequent free tree discovery in graph data
Proceedings of the 2004 ACM symposium on Applied computing
IncSpan: incremental mining of sequential patterns in large database
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Mining interesting knowledge from weblogs: a survey
Data & Knowledge Engineering
An efficient algorithm for mining closed inter-transaction itemsets
Data & Knowledge Engineering
Data & Knowledge Engineering
Mining globally distributed frequent subgraphs in a single labeled graph
Data & Knowledge Engineering
Mining closed patterns in multi-sequence time-series databases
Data & Knowledge Engineering
Depth first generation of frequent patterns without candidate generation
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Top-down and bottom-up strategies for incremental maintenance of frequent patterns
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Data mining for adaptive learning in a TESL-based e-learning system
Expert Systems with Applications: An International Journal
Mining frequent patterns from univariate uncertain data
Data & Knowledge Engineering
Context-aware inference in ubiquitous residential environments
Computers in Industry
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Sequential pattern mining is crucial to data mining domains. This paper proposes a novel data mining approach for exploring hierarchical tree structures, named tree-like patterns, representing the relationships for a pair of items in a sequence. Using tree-like patterns, the relationships for a pair of items can be identified in terms of the cause and effect. A novel technique that efficiently counts support values for tree-like patterns using a queue structure is proposed. In addition, this paper addresses an efficient scheme for determining the frequency of a tree-like pattern in a sequence using a dynamic programming approach. Each tree-like pattern embedded in a sequence is considered to have a certain valuable meaning or the degree of importance used in different applications. Two addressed formulas are applied to determine the degree of significance for a specific sequence, which denotes the degree of consecutive items in a tree-like pattern for a sequence. The larger the degree of significance a tree-like pattern has, the more the tree-like pattern is compacted in the sequence. The characteristics differentiating the explored patterns from those obtained with other schemes are discussed. A simulation analysis of the proposed data mining approach is utilized to demonstrate its efficacy. Finally, the proposed approach is designed and implemented in a data mining system integrated into a novel e-learning platform.