Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering knowledge from large databases using prestored information
Information Systems
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
An Interval Classifier for Database Mining Applications
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamically maintaining frequent items over a data stream
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Incremental rule learning based on example nearness from numerical data streams
Proceedings of the 2005 ACM symposium on Applied computing
Approximately Processing Multi-granularity Aggregate Queries over Data Streams
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Catch the moment: maintaining closed frequent itemsets over a data stream sliding window
Knowledge and Information Systems
Data Streams: Models and Algorithms (Advances in Database Systems)
Data Streams: Models and Algorithms (Advances in Database Systems)
Incremental Mining of Sequential Patterns over a Stream Sliding Window
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Query languages and data models for database sequences and data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Infrequent Item Mining in Multiple Data Streams
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
A dynamic layout of sliding window for frequent itemset mining over data streams
Journal of Systems and Software
Towards a variable size sliding window model for frequent itemset mining over data streams
Computers and Industrial Engineering
Efficient mining of frequent items coupled with weight and /or support over progressive databases
ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
Effective product assignment based on association rule mining in retail
Expert Systems with Applications: An International Journal
UT-Tree: Efficient mining of high utility itemsets from data streams
Intelligent Data Analysis
Efficient mining of maximal correlated weight frequent patterns
Intelligent Data Analysis
Hi-index | 12.05 |
In recent years, data stream mining has become an important research topic. With the emergence of new applications, the data we process are not again static, but the continuous dynamic data stream. Examples include network traffic analysis, Web click stream mining, network intrusion detection, and on-line transaction analysis. In this paper, we propose a new framework for data stream mining, called the weighted sliding window model. The proposed model allows the user to specify the number of windows for mining, the size of a window, and the weight for each window. Thus users can specify a higher weight to a more significant data section, which will make the mining result closer to user's requirements. Based on the weighted sliding window model, we propose a single pass algorithm, called WSW, to efficiently discover all the frequent itemsets from data streams. By analyzing data characteristics, an improved algorithm, called WSW-Imp, is developed to further reduce the time of deciding whether a candidate itemset is frequent or not. Empirical results show that WSW-Imp outperforms WSW under the weighted sliding window model.