Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 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
Finding Frequent Items in Data Streams
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
A simple algorithm for finding frequent elements in streams and bags
ACM Transactions on Database Systems (TODS)
What's hot and what's not: tracking most frequent items dynamically
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Identifying frequent items in sliding windows over on-line packet streams
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
On demand classification of data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Online Mining (Recently) Maximal Frequent Itemsets over Data Streams
RIDE '05 Proceedings of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications
Research issues in data stream association rule mining
ACM SIGMOD Record
CFI-Stream: mining closed frequent itemsets in data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Mining maximal frequent itemsets from data streams
Journal of Information Science
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Efficient Mining of Frequent Patterns from Uncertain Data
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Approximate mining of frequent patterns on streams
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
An efficient algorithm for mining temporal high utility itemsets from data streams
Journal of Systems and Software
Mining frequent itemsets over data streams using efficient window sliding techniques
Expert Systems with Applications: An International Journal
Mining of Frequent Itemsets from Streams of Uncertain Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Frequent pattern mining with uncertain data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent itemsets from uncertain data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
A decremental approach for mining frequent itemsets from uncertain data
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A tree-based approach for frequent pattern mining from uncertain data
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Mining uncertain data with probabilistic guarantees
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
PutMode: prediction of uncertain trajectories in moving objects databases
Applied Intelligence
A regression-based approach for mining user movement patterns from random sample data
Data & Knowledge Engineering
HUC-Prune: an efficient candidate pruning technique to mine high utility patterns
Applied Intelligence
Mining frequent patterns from univariate uncertain data
Data & Knowledge Engineering
A false negative approach to mining frequent itemsets from high speed transactional data streams
Information Sciences: an International Journal
Mining frequent patterns from dynamic data streams with data load management
Journal of Systems and Software
Efficient fuzzy ranking queries in uncertain databases
Applied Intelligence
Data stream classification with artificial endocrine system
Applied Intelligence
Constrained frequent pattern mining on univariate uncertain data
Journal of Systems and Software
Incrementally mining high utility patterns based on pre-large concept
Applied Intelligence
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In this paper, we propose mining frequent patterns from univariate uncertain data streams, which have a quantitative interval for each attribute in a transaction and a probability density function indicating the possibilities that the values in the interval appear. Many data streams comprise flows of univariate uncertain data, for example, the records of atmospheric pollution sensors, and network monitoring records. We propose two algorithms to address this issue: the ExactU2Stream algorithm and the ApproxiU2Stream algorithm. The former incrementally stores the incoming transactions, and delays the mining process until it is requested. The latter mines the transactions immediately when they arrive, and stores the derived frequent patterns. Compared with the latter, the former returns results that are more accurate, but it also requires more response time. Both algorithms utilize the sliding window scheme, which decomposes the continuous data stream into discrete, overlapping chunks. The proposed algorithms outperform the compared methods in terms of runtime and memory usage. We have applied the two proposed algorithms to the data streams recording the air quality in Taiwan; the derived frequent patterns not only show the common air quality in Taiwan but also show the extremely bad air quality when a sand storm affects Taiwan.