A survey on learning from data streams: current and future trends

Nowadays, there are applications in which the data are modeled best not as persistent tables, but rather as transient data streams. In this article, we discuss the limitations of current machine learning and data mining algorithms. We discuss the fundamental issues in learning in dynamic environment...

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Bibliographic Details
Published in:Progress in artificial intelligence 2012-04, Vol.1 (1), p.45-55
Main Author: Gama, João
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computational Intelligence
Computer Imaging
Computer Science
Control
Data Mining and Knowledge Discovery
Mechatronics
Natural Language Processing (NLP)
Pattern Recognition and Graphics
Review
Robotics
Vision
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Summary:Nowadays, there are applications in which the data are modeled best not as persistent tables, but rather as transient data streams. In this article, we discuss the limitations of current machine learning and data mining algorithms. We discuss the fundamental issues in learning in dynamic environments like continuously maintain learning models that evolve over time, learning and forgetting, concept drift and change detection. Data streams produce a huge amount of data that introduce new constraints in the design of learning algorithms: limited computational resources in terms of memory, cpu power, and communication bandwidth. We present some illustrative algorithms, designed to taking these constrains into account, for decision-tree learning, hierarchical clustering and frequent pattern mining. We identify the main issues and current challenges that emerge in learning from data streams that open research lines for further developments.
ISSN:2192-6352
2192-6360
DOI:10.1007/s13748-011-0002-6