An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Warehousing and mining Web logs
Proceedings of the 2nd international workshop on Web information and data management
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient mining of weighted association rules (WAR)
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining patterns from graph traversals
Data & Knowledge Engineering
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
A Graph-Based Approach for Discovering Various Types of Association Rules
IEEE Transactions on Knowledge and Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining frequent item sets by opportunistic projection
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
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
On Closed Constrained Frequent Pattern Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
WTPMiner: efficient mining of weighted frequent patterns based on graph traversals
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
WLPMiner: weighted frequent pattern mining with length-decreasing support constraints
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Full duplicate candidate pruning for frequent connected subgraph mining
Integrated Computer-Aided Engineering
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Data mining for traversal patterns has been found useful in many applications. Traditional methods of traversal patterns mining only considered unweighted traversals. The support measure in weighted cases no longer satisfies the anti-monotone property and previous algorithms cannot be applied. In this paper, a transformational model between EWDG (Edge-Weighted Directed Graph) and VWDG (Vertex-Weighted Directed Graph) is proposed. Based on the model, we explore the problem of mining interesting patterns - frequent patterns and closed frequent patterns form traversals on WDG and propose two algorithms respectively called WTFPMiner (Weighted Traversal Frequent Patterns Miner) and CWTFPMiner (Closed Weighted Traversal Frequent Patterns Miner). WTFPMiner devises a novel property between weighted patterns - scalable property, which is similar to the well-known downward closure property, to convert weighted pattern mining problem into corresponding pattern scalability decision problem, and finally exploits this property to mine the weighted frequent patterns efficiently from traversals on WDG. Based on an improved model of weighted support, algorithm CWTFPMiner adopts a divide-and-conquer paradigm in a pattern growth manner to mine closed weighted frequent patterns efficiently from the traversals on weighted directed graph. It incorporates the closure property with weight constrain to dramatically reduce search space and extracts succinct and lossless patterns from traversal transactions of WDG. Our performance study shows that the two algorithms are efficient and scalable for the problem of mining frequent patterns based on weighted directed graph traversals. Moreover, they can be applied in diversiform environments which can be modeled as a WDG.