Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient algorithm to update large itemsets with early pruning
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental and interactive sequence mining
Proceedings of the eighth international conference on Information and knowledge management
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Sliding-window filtering: an efficient algorithm for incremental mining
Proceedings of the tenth international conference on Information and knowledge management
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
Mining Association Rules: Anti-Skew Algorithms
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Dynamically maintaining frequent items over a data stream
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
IncSpan: incremental mining of sequential patterns in large database
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Online Mining (Recently) Maximal Frequent Itemsets over Data Streams
RIDE '05 Proceedings of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications
A regression-based temporal pattern mining scheme for data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Mining closed flexible patterns in time-series databases
Expert Systems with Applications: An International Journal
An improved association rules mining method
Expert Systems with Applications: An International Journal
Incremental Algorithm for Discovering Frequent Subsequences in Multiple Data Streams
International Journal of Data Warehousing and Mining
| Hi-index | 12.05 |
This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the editor as the authors have plagiarized part of a paper that had already appeared in An Efficient Method For Finding Emerging Large Itemsets, published by the Association for Computers and Machinery (ACM) in the proceedings of the KDD Temporal Data Mining Workshop in August of 2004. (An Efficient Method For Finding Emerging Large Itemsets, Susan P. Imberman, Abdullah Uz Tansel, Eric Pacuit, The Third Workshop on Mining Temporal and Sequential Data, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2004. One of the conditions of submission of a paper for publication is that authors declare explicitly that their work is original and has not appeared in a publication elsewhere. Re-use of any data should be appropriately cited. As such this article represents a severe abuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process.