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Data stream management and mining
2008This paper provides an introduction to the field of data stream management and mining. The increase of data production in operational information systems prevents from monitoring these systems with the old paradigm of storing data before analyzing it.
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On Smart Mobility and Data Stream Mining
20220 ...
Javier J. Sánchez Medina +4 more
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2006
Knowledge discovery from infinite data streams is an important and difficult task. We are facing two challenges, the overwhelming volume and the concept drifts of the streaming data. In this chapter, we introduce a general framework for mining concept-drifting data streams using weighted ensemble classifiers.
Haixun Wang, Philip S. Yu, Jiawei Han
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Knowledge discovery from infinite data streams is an important and difficult task. We are facing two challenges, the overwhelming volume and the concept drifts of the streaming data. In this chapter, we introduce a general framework for mining concept-drifting data streams using weighted ensemble classifiers.
Haixun Wang, Philip S. Yu, Jiawei Han
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2008
Data analysis or data mining have been applied to data produced by many kinds of systems. Some systems, for example road traffic monitoring, produce data continuously, and often at high rates. Analyzing such data creates new issues, because it is neither appropriate, nor perhaps possible, to accumulate it and process it using standard data-mining ...
Hanady Abdulsalam +2 more
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Data analysis or data mining have been applied to data produced by many kinds of systems. Some systems, for example road traffic monitoring, produce data continuously, and often at high rates. Analyzing such data creates new issues, because it is neither appropriate, nor perhaps possible, to accumulate it and process it using standard data-mining ...
Hanady Abdulsalam +2 more
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Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, 2000Many organizations today have more than very large databases; they have databases that grow without limit at a rate of several million records per day. Mining these continuous data streams brings unique opportunities, but also new challenges. This paper describes and evaluates VFDT, an anytime system that builds decision trees using constant memory and
Pedro M. Domingos, Geoff Hulten
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Energy Efficiency in Data Stream Mining
Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, 2015Data mining algorithms are usually designed to optimize a trade-off between predictive accuracy and computational efficiency. This paper introduces energy consumption and energy efficiency as important factors to consider during data mining algorithm analysis and evaluation.
Eva García Martín +2 more
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Decision trees for mining data streams
Intelligent Data Analysis, 2006In this paper we study the problem of constructing accurate decision tree models from data streams. Data streams are incremental tasks that require incremental, online, and any-time learning algorithms. One of the most successful algorithms for mining data streams is VFDT.
João Gama 0001 +2 more
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Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, 2001Most statistical and machine-learning algorithms assume that the data is a random sample drawn from a stationary distribution. Unfortunately, most of the large databases available for mining today violate this assumption. They were gathered over months or years, and the underlying processes generating them changed during this time, sometimes radically.
Geoff Hulten +2 more
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Data Stream Clustering Algorithm in Data Mining
2020With the rapid development of Internet technology and services, data flow models have entered the field of data mining. Cluster analysis, as an important issue in the field of data flow mining, has been highly valued and widely studied in the recent past.
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Rare Pattern Mining on Data Streams
2012There has been some research in the area of rare pattern mining where the researchers try to capture patterns involving events that are unusual in a dataset. These patterns are considered more useful than frequent patterns in some domain, including detection of computer attacks, or fraudulent credit transactions. Until now, most of the research in this
David Tse Jung Huang +2 more
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