Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Rule Discovery in Telecommunication AlarmData
Journal of Network and Systems Management
Efficient mining of weighted association rules (WAR)
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Finding Interesting Associations without Support Pruning
IEEE Transactions on Knowledge and Data Engineering
Knowledge Discovery from Telecommunication Network Alarm Databases
ICDE '96 Proceedings of the Twelfth 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
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th 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
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
A Transaction Mapping Algorithm for Frequent Itemsets Mining
IEEE Transactions on Knowledge and Data Engineering
An efficient algorithm for mining frequent inter-transaction patterns
Information Sciences: an International Journal
Efficient mining of weighted interesting patterns with a strong weight and/or support affinity
Information Sciences: an International Journal
Discovery of maximum length frequent itemsets
Information Sciences: an International Journal
Bottom-up discovery of frequent rooted unordered subtrees
Information Sciences: an International Journal
ICICTA '08 Proceedings of the 2008 International Conference on Intelligent Computation Technology and Automation - Volume 01
IMine: Index Support for Item Set Mining
IEEE Transactions on Knowledge and Data Engineering
Looking into the seeds of time: Discovering temporal patterns in large transaction sets
Information Sciences: an International Journal
IEEE Transactions on Network and Service Management
PARM—An Efficient Algorithm to Mine Association Rules From Spatial Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Cosine interesting pattern discovery
Information Sciences: an International Journal
Weighted association rule mining via a graph based connectivity model
Information Sciences: an International Journal
Inference of network anomaly propagation using spatio-temporal correlation
Journal of Network and Computer Applications
Mining numerical association rules via multi-objective genetic algorithms
Information Sciences: an International Journal
Scaling up cosine interesting pattern discovery: A depth-first method
Information Sciences: an International Journal
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Alarm correlation analysis system is an useful method and tool for analyzing alarms and finding the root cause of faults in telecommunication networks. Recently, the application of association rules mining becomes an important research area in alarm correlation analysis. In this paper, we propose a novel Association Rules Mining based Alarm Correlation Analysis System (ARM-ACAS) to find interesting association rules between alarm events. In order to mine some infrequent but important items, ARM-ACAS first uses neural network to classify the alarms with different levels. In addition, ARM-ACAS also exploits an optimization technique with the weighted frequent pattern tree structure to improve the mining efficiency. The system is both efficient and practical in discovering significant relationships of alarms as illustrated by experiments performed on simulated and real-world datasets.