Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Efficient Mining of Association Rules in Distributed Databases
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 Access Patterns Efficiently from Web Logs
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Mining Web Log Sequential Patterns with Position Coded Pre-Order Linked WAP-Tree
Data Mining and Knowledge Discovery
Frequent pattern mining from time-fading streams of uncertain data
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Hi-index | 0.00 |
Since transaction identifiers (ids) are unique and would not usually be frequent, mining frequent patterns with transaction ids, showing records they occurred in, provides an efficient way to mine frequent patterns in many types of databases including multiple tabled and distributed databases. Existing work have not focused on mining frequent patterns with the transaction ids they occurred in. Many applications require finding strong associations between transaction id (e.g., certain drug) and the itemsets (e.g., certain adverse effects) to help deduce some pertinent lacking information (like how many people use this product in total) and information (like how many people have the adverse effects). This paper proposes a set of algorithms TidFPs, for mining frequent patterns with their transaction ids in a single transaction database, in a multiple tabled database, and in a distributed database. The proposed technique scans the database records only once even with level-wise Apriori-based mining techniques, stores frequent 1-items with their transaction id bitmap, outperforms traditional approaches and is extendible to other tree-based mining techniques as well as sequential mining.