Tutorial: Data Stream Mining and Its Applications

  • Latifur Khan
  • Wei Fan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7239)

Abstract

Data streams are continuous flows of data. Examples of data streams include network traffic, sensor data, call center records and so on. Their sheer volume and speed pose a great challenge for the data mining community to mine them. Data streams demonstrate several unique properties: infinite length, concept-drift, concept-evolution, feature-evolution and limited labeled data. Concept-drift occurs in data streams when the underlying concept of data changes over time. Concept-evolution occurs when new classes evolve in streams. Feature-evolution occurs when feature set varies with time in data streams. Data streams also suffer from scarcity of labeled data since it is not possible to manually label all the data points in the stream. Each of these properties adds a challenge to data stream mining.

Multi-step methodologies and techniques, and multi-scan algorithms, suitable for knowledge discovery and data mining, cannot be readily applied to data streams. This is due to well-known limitations such as bounded memory, high speed data arrival, online/timely data processing, and need for one-pass techniques (i.e., forgotten raw data) issues etc. In spite of the success and extensive studies of stream mining techniques, there is no single tutorial dedicated to a unified study of the new challenges introduced by evolving stream data like change detection, novelty detection, and feature evolution. This tutorial presents an organized picture on how to handle various data mining techniques in data streams: in particular, how to handle classification and clustering in evolving data streams by addressing these challenges. The importance and significance of research in data stream mining has been manifested in most recent launch of large scale stream processing prototype in many important application areas. In the same time, commercialization of streams (e.g., IBM InfoSphere streams, etc.) brings new challenge and research opportunities to the Data Mining (DM) community. In this tutorial a number of applications of stream mining will be presented such as adaptive malicious code detection, on-line malicious URL detection, evolving insider threat detection and textual stream classification.

Keywords

Data Stream Novelty Detection Stream Mining Data Stream Mining Data Mining Community 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Latifur Khan
    • 1
  • Wei Fan
    • 2
  1. 1.University of TexasDallasUSA
  2. 2.IBM T.J. Watson ResearchUSA

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