Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Online Algorithms for Mining Semi-structured Data Stream
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth 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
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A regression-based temporal pattern mining scheme for data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Encoding probability propagation in belief networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A semi-random multiple decision-tree algorithm for mining data streams
Journal of Computer Science and Technology
Mining frequent closed itemsets from a landmark window over online data streams
Computers & Mathematics with Applications
Mining non-derivable frequent itemsets over data stream
Data & Knowledge Engineering
A false negative maximal frequent itemset mining algorithm over stream
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Computers & Mathematics with Applications
Stream mining on univariate uncertain data
Applied Intelligence
Mining maximal frequent patterns by considering weight conditions over data streams
Knowledge-Based Systems
Efficient mining of maximal correlated weight frequent patterns
Intelligent Data Analysis
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Frequent pattern mining from data streams is an active research topic in data mining. Existing research efforts often rely on a two-phase framework to discover frequent patterns: (1) using internal data structures to store meta-patterns obtained by scanning the stream data; and (2) re-mining the meta-patterns to finalize and output frequent patterns. The defectiveness of such a two-phase framework lies in the fact that the two stages provide barriers to dynamically and immediately finding frequent patterns with online functionalities. It is expected that a single-phase algorithm can fulfil frequent pattern mining from data streams in such a way that the users can see patterns in an immediate and dynamic manner, as soon as the patterns have become frequent. In this paper, we propose INSTANT, a single-phase algorithm for discovering frequent itemsets from data streams. The theoretical foundation of INSTANT is based on a framework theory on a set of itemsets, which is also presented in the paper. The novel design of INSTANT ensures that it employs compact data structures to mine frequent patterns from data streams in a single phase. Our experimental results demonstrate the time and space efficiency of the proposed algorithm.