Data stream mining using granularity-based approach

M. Gaber

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review


Significant applications require data stream mining algorithms to run in resource-constrained environments. Thus, adaptation is a key process to ensure the consistency and continuity of the running algorithms. This chapter provides a theoretical framework for applying the granularity-based approach in mining data streams. Our Algorithm Output Granularity (AOG) is explained in details providing practitioners the ability to use it for enabling resource-awareness and adaptability for their algorithms. Theoretically, AOG has been formalized using the Probably Approximately Correct (PAC) learning model allowing researchers to formalize the adaptability of their techniques. Finally, the integration of AOG with other adaptation strategies is provided.
Original languageEnglish
Title of host publicationFoundations of Computational Intelligence (6)
Place of PublicationBerlin / Heidelberg
Number of pages20
ISBN (Print)9783642010903
Publication statusPublished - 2009

Publication series

NameStudies in Computational Intelligence


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