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Mining frequent itemsets in a stream
- Source :
- Information Systems, 39(1), 233-255. Elsevier, Information systems
- Publication Year :
- 2014
- Publisher :
- Elsevier BV, 2014.
-
Abstract
- Mining frequent itemsets in a datastream proves to be a difficult problem, as itemsets arrive in rapid succession and storing parts of the stream is typically impossible. Nonetheless, it has many useful applications; e.g., opinion and sentiment analysis from social networks. Current stream mining algorithms are based on approximations. In earlier work, mining frequent items in a stream under the max-frequency measure proved to be effective for items. In this paper, we extended our work from items to itemsets. Firstly, an optimized incremental algorithm for mining frequent itemsets in a stream is presented. The algorithm maintains a very compact summary of the stream for selected itemsets. Secondly, we show that further compacting the summary is non-trivial. Thirdly, we establish a connection between the size of a summary and results from number theory. Fourthly, we report results of extensive experimentation, both of synthetic and real-world datasets, showing the efficiency of the algorithm both in terms of time and space. (C) 2012 Elsevier Ltd. All rights reserved.
- Subjects :
- Computer. Automation
Measure (data warehouse)
Computer science
Data stream mining
Sentiment analysis
InformationSystems_DATABASEMANAGEMENT
computer.software_genre
Frequent itemset mining
Datastream
Theory
Algorithm
Experiments
ComputingMethodologies_PATTERNRECOGNITION
Hardware and Architecture
Data mining
computer
Software
Information Systems
Subjects
Details
- ISSN :
- 03064379
- Volume :
- 39
- Database :
- OpenAIRE
- Journal :
- Information Systems
- Accession number :
- edsair.doi.dedup.....7e8633f830f9c99afa3032cdfbcfdd3b
- Full Text :
- https://doi.org/10.1016/j.is.2012.01.005