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Data stream mining using granularity-based approach

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

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Data stream mining using granularity-based approach. / Gaber, M.

Foundations of Computational Intelligence (6). 206. ed. Berlin / Heidelberg : Springer, 2009. p. 47-66 (Studies in Computational Intelligence; No. 206).

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

Harvard

Gaber, M 2009, Data stream mining using granularity-based approach. in Foundations of Computational Intelligence (6). 206 edn, Studies in Computational Intelligence, no. 206, Springer, Berlin / Heidelberg, pp. 47-66.

APA

Gaber, M. (2009). Data stream mining using granularity-based approach. In Foundations of Computational Intelligence (6) (206 ed., pp. 47-66). (Studies in Computational Intelligence; No. 206). Springer.

Vancouver

Gaber M. Data stream mining using granularity-based approach. In Foundations of Computational Intelligence (6). 206 ed. Berlin / Heidelberg: Springer. 2009. p. 47-66. (Studies in Computational Intelligence; 206).

Author

Gaber, M. / Data stream mining using granularity-based approach. Foundations of Computational Intelligence (6). 206. ed. Berlin / Heidelberg : Springer, 2009. pp. 47-66 (Studies in Computational Intelligence; 206).

Bibtex

@inbook{688ec3db373e4b11ac6e2f73ca03faf3,
title = "Data stream mining using granularity-based approach",
abstract = "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.",
author = "M. Gaber",
year = "2009",
language = "English",
isbn = "9783642010903",
series = "Studies in Computational Intelligence",
publisher = "Springer",
number = "206",
pages = "47--66",
booktitle = "Foundations of Computational Intelligence (6)",
edition = "206",

}

RIS

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T1 - Data stream mining using granularity-based approach

AU - Gaber, M.

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

M3 - Chapter (peer-reviewed)

SN - 9783642010903

T3 - Studies in Computational Intelligence

SP - 47

EP - 66

BT - Foundations of Computational Intelligence (6)

PB - Springer

CY - Berlin / Heidelberg

ER -

ID: 70764