Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
GPSR: greedy perimeter stateless routing for wireless networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Data mining: concepts and techniques
Data mining: concepts and techniques
Parallel and Distributed Association Mining: A Survey
IEEE Concurrency
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A High-Performance Distributed Algorithm for Mining Association Rules
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Finding (Recently) Frequent Items in Distributed Data Streams
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Approximate counts and quantiles over sliding windows
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
ODAM: An Optimized Distributed Association Rule Mining Algorithm
IEEE Distributed Systems Online
Online algorithms for mining inter-stream associations from large sensor networks
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
| Hi-index | 0.00 |
Mining Frequent Items from sensory data is a major research problem in wireless sensor networks(WSNs) and it can be widely used in environmental monitoring. Conventional Lossy Counting algorithm can be applied to solve this problem in centralized manner. However, centralized algorithm brings severely data collision in WSNs, and results in inaccurate mining results. In this paper, we present D-FIMA, a distributed frequent items mining algorithm. D-FIMA, running at every sensor node, establishes items aggregation tree via forwarding mining request beforehand, and each node maintains local approximate frequent items. The root of the aggregation tree outputs the final global approximate frequent items. Theoretical analysis and the simulation results show that energy consumption of D-FIMA is much less than the centralized algorithm, and mining results of D-FIMA is more accurate than the centralized algorithm.