Sensitivity Analysis of Proactive Data Mining

Haim Dahan; Shahar Cohen; Lior Rokach; Oded Maimon

doi:10.1007/978-1-4939-0539-3_5

Sensitivity Analysis of Proactive Data Mining

Haim Dahan
, Shahar Cohen
, Lior Rokach
, Oded Maimon

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

As stated in Chap. 4, to achieve an effective and applicable solution for a data mining problem, it is vital to thoroughly understand the problem at hand, in particular its constraints, environment and its problem specific knowledge. However, it is difficult to pinpoint the exact knowledge (i.e., attribute values) necessary for optimally implementing the proactive data mining method. In this chapter we present several scenarios over the security company’s case (Chap. 4) to demonstrate the general boundaries of the method.

Original language	English
Title of host publication	PROACTIVE DATA MINING WITH DECISION TREES
Pages	63-85
ISBN (Electronic)	978-1-4939-0539-3
DOIs	https://doi.org/10.1007/978-1-4939-0539-3_5
State	Published - 15 Feb 2014

Publication series

Name	SpringerBriefs in Electrical and Computer Engineering
ISSN (Print)	2191-8112
ISSN (Electronic)	2191-8120

Keywords

Proactive data mining
Sensitivity analysis

Access to Document

10.1007/978-1-4939-0539-3_5

Cite this

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title = "Sensitivity Analysis of Proactive Data Mining",

abstract = "As stated in Chap. 4, to achieve an effective and applicable solution for a data mining problem, it is vital to thoroughly understand the problem at hand, in particular its constraints, environment and its problem specific knowledge. However, it is difficult to pinpoint the exact knowledge (i.e., attribute values) necessary for optimally implementing the proactive data mining method. In this chapter we present several scenarios over the security company{\textquoteright}s case (Chap. 4) to demonstrate the general boundaries of the method.",

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AU - Cohen, Shahar

AU - Rokach, Lior

AU - Maimon, Oded

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AB - As stated in Chap. 4, to achieve an effective and applicable solution for a data mining problem, it is vital to thoroughly understand the problem at hand, in particular its constraints, environment and its problem specific knowledge. However, it is difficult to pinpoint the exact knowledge (i.e., attribute values) necessary for optimally implementing the proactive data mining method. In this chapter we present several scenarios over the security company’s case (Chap. 4) to demonstrate the general boundaries of the method.

KW - Proactive data mining

KW - Sensitivity analysis

U2 - 10.1007/978-1-4939-0539-3_5

DO - 10.1007/978-1-4939-0539-3_5

M3 - Chapter

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T3 - SpringerBriefs in Electrical and Computer Engineering

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BT - PROACTIVE DATA MINING WITH DECISION TREES

ER -

Sensitivity Analysis of Proactive Data Mining

Abstract

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Keywords

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