Online association rule mining
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Visualization of navigation patterns on a Web site using model-based clustering
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Indirect Association: Mining Higher Order Dependencies in Data
PKDD '00 Proceedings of the 4th European Conference 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
Mining Indirect Associations in Web Data
WEBKDD '01 Revised Papers from the Third International Workshop on Mining Web Log Data Across All Customers Touch Points
On the Mining of Substitution Rules for Statistically Dependent Items
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
estWin: adaptively monitoring the recent change of frequent itemsets over online data streams
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
An Algorithm for In-Core Frequent Itemset Mining on Streaming Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Finding Maximal Frequent Itemsets over Online Data Streams Adaptively
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
The Influence of Indirect Association Rules on Recommendation Ranking Lists
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
CFI-Stream: mining closed frequent itemsets in data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient approach to mining indirect associations
Journal of Intelligent Information Systems
Data Streams: Models and Algorithms (Advances in Database Systems)
Data Streams: Models and Algorithms (Advances in Database Systems)
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Mining temporal indirect associations
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
An approximate approach for mining recently frequent itemsets from data streams
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
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An indirect association refers to an infrequent itempair, each item of which is highly co-occurring with a frequent itemset called "mediator". Although indirect associations have been recognized as powerful patterns in revealing interesting information hidden in many applications, such as recommendation ranking, substitute items or competitive items, and common web navigation path, etc., almost no work, to our knowledge, has investigated how to discover this type of patterns from streaming data. In this paper, the problem of mining indirect associations from data streams is considered. Unlike contemporary research work on stream data mining that investigates the problem individually from different types of streaming models, we treat the problem in a generic way. We propose a generic window model that can represent all classical streaming models and retain user flexibility in defining new models. In this context, a generic algorithm is developed, which guarantees no false positive rules and bounded support error as long as the window model is specifiable by the proposed generic model. Comprehensive experiments on both synthetic and real datasets have showed the effectiveness of the proposed approach as a generic way for finding indirect association rules over streaming data.