Introduction to algorithms
A new and versatile method for association generation
Information Systems
Online association rule mining
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Turbo-charging vertical mining of large databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
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
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Online Generation of Association Rules
ICDE '98 Proceedings of the Fourteenth 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
Online and Incremental Mining of Separately-Grouped Web Access Logs
WISE '02 Proceedings of the 3rd International Conference on Web Information Systems Engineering
Summary Structures for Frequency Queries on Large Transaction Sets
DCC '00 Proceedings of the Conference on Data Compression
Fast Online Dynamic Association Rule Mining
WISE '01 Proceedings of the Second International Conference on Web Information Systems Engineering (WISE'01) Volume 1 - Volume 1
k-RNN: k-relational nearest neighbour algorithm
Proceedings of the 2008 ACM symposium on Applied computing
Mining frequent patterns from network flows for monitoring network
Expert Systems with Applications: An International Journal
Function and service pattern analysis for facilitating the reconfiguration of collaboration systems
Computers and Industrial Engineering
Mop: An Efficient Algorithm for Mining Frequent Pattern with Subtree Traversing
Fundamenta Informaticae
Scalable technique to discover items support from trie data structure
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
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The importance of data mining is apparent with the advent of powerful data collection and storage tools; raw data is so abundant that manual analysis is no longer possible. Unfortunately, data mining problems are difficult to solve and this prompted the introduction of several novel data structures to improve mining efficiency. Here, we will critically examine existing preprocessing data structures used in association rule mining for enhancing performance in an attempt to understand their strengths and weaknesses. Our analyses culminate in a practical structure called the SOTrieIT (Support-Ordered Trie Itemset) and two synergistic algorithms to accompany it for the fast discovery of frequent itemsets. Experiments involving a wide range of synthetic data sets reveal that its algorithms outperform FP-growth, a recent association rule mining algorithm with excellent performance, by up to two orders of magnitude and, thus, verifying its efficiency and viability.