Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
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
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
WAR: Weighted Association Rules for Item Intensities
Knowledge and Information Systems
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
estWin: Online data stream mining of recent frequent itemsets by sliding window method
Journal of Information Science
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
Efficient calendar based temporal association rule
ACM SIGMOD Record
Research issues in data stream association rule mining
ACM SIGMOD Record
Data Mining and Knowledge Discovery
An integrated efficient solution for computing frequent and top-k elements in data streams
ACM Transactions on Database Systems (TODS)
Mining lossless closed frequent patterns with weight constraints
Knowledge-Based Systems
Towards a new approach for mining frequent itemsets on data stream
Journal of Intelligent Information Systems
CanTree: a canonical-order tree for incremental frequent-pattern mining
Knowledge and Information Systems
BitTableFI: An efficient mining frequent itemsets algorithm
Knowledge-Based Systems
EDUA: An efficient algorithm for dynamic database mining
Information Sciences: an International Journal
Efficient mining of weighted interesting patterns with a strong weight and/or support affinity
Information Sciences: an International Journal
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Discovery of maximum length frequent itemsets
Information Sciences: an International Journal
Incremental and interactive mining of web traversal patterns
Information Sciences: an International Journal
A new framework for detecting weighted sequential patterns in large sequence databases
Knowledge-Based Systems
Index-BitTableFI: An improved algorithm for mining frequent itemsets
Knowledge-Based Systems
An efficient mining of weighted frequent patterns with length decreasing support constraints
Knowledge-Based Systems
Efficient algorithms for incremental maintenance of closed sequential patterns in large databases
Data & Knowledge Engineering
Efficient single-pass frequent pattern mining using a prefix-tree
Information Sciences: an International Journal
Mining Weighted Frequent Patterns in Incremental Databases
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
The Pre-FUFP algorithm for incremental mining
Expert Systems with Applications: An International Journal
Interactive mining of top-K frequent closed itemsets from data streams
Expert Systems with Applications: An International Journal
Mining frequent closed patterns in pointset databases
Information Systems
Processing online analytics with classification and association rule mining
Knowledge-Based Systems
A fast algorithm for maintenance of association rules in incremental databases
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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
A false negative approach to mining frequent itemsets from high speed transactional data streams
Information Sciences: an International Journal
Effective periodic pattern mining in time series databases
Expert Systems with Applications: An International Journal
Sliding window based weighted maximal frequent pattern mining over data streams
Expert Systems with Applications: An International Journal
Mining maximal frequent patterns by considering weight conditions over data streams
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Efficient mining of maximal correlated weight frequent patterns
Intelligent Data Analysis
| Hi-index | 12.06 |
Weighted frequent pattern (WFP) mining is more practical than frequent pattern mining because it can consider different semantic significance (weight) of the items. For this reason, WFP mining becomes an important research issue in data mining and knowledge discovery. However, existing algorithms cannot be applied for incremental and interactive WFP mining and also for stream data mining because they are based on a static database and require multiple database scans. In this paper, we present two novel tree structures IWFPT"W"A (Incremental WFP tree based on weight ascending order) and IWFPT"F"D (Incremental WFP tree based on frequency descending order), and two new algorithms IWFP"W"A and IWFP"F"D for incremental and interactive WFP mining using a single database scan. They are effective for incremental and interactive mining to utilize the current tree structure and to use the previous mining results when a database is updated or a minimum support threshold is changed. IWFP"W"A gets advantage in candidate pattern generation by obtaining the highest weighted item in the bottom of IWFPT"W"A. IWFP"F"D ensures that any non-candidate item cannot appear before candidate items in any branch of IWFPT"F"D and thus speeds up the prefix tree and conditional tree creation time during mining operation. IWFPT"F"D also achieves the highly compact incremental tree to save memory space. To our knowledge, this is the first research work to perform single-pass incremental and interactive mining for weighted frequent patterns. Extensive performance analyses show that our tree structures and algorithms are very efficient and scalable for single-pass incremental and interactive WFP mining.