Quality Measures in Data Mining

Fabrice Guillet

ISBN 10: 3540449116 / ISBN 13: 9783540449119

Verlag: Springer, Berlin, Springer Berlin Heidelberg, Springer, 2007

Beschreibung

This item is printed on demand - it takes 3-4 days longer - Neuware -Data mining analyzes large amounts of data to discover knowledge relevant to decision making. Typically, numerous pieces of knowledge are extracted by a data mining system and presented to a human user, who may be a decision-maker or a data-analyst. The user is confronted with the task of selecting the pieces of knowledge that are of the highest quality or interest according to his or her preferences. Since this selection is sometimes a daunting task, designing quality and interestingness measures has become an important challenge for data mining researchers in the last decade.This volume presents the state of the art concerning quality and interestingness measures for data mining. The book summarizes recent developments and presents original research on this topic. The chapters include surveys, comparative studies of existing measures, proposals of new measures, simulations, and case studies. Both theoretical and applied chapters are included. Papers for this book were selected and reviewed for correctness and completeness by an international review committee. 314 pp. Englisch.

Bestandsnummer des Verkäufers 9783540449119

Bibliografische Details

• Titel: Quality Measures in Data Mining
• Verlag: Springer, Berlin, Springer Berlin Heidelberg, Springer
• Erscheinungsjahr: 2007
• Sprache: Englisch
• ISBN-10: 3540449116
• ISBN-13: 9783540449119
• Einband: Buch
• Zustand: Neu
• Abmessungen: 235B x 155 H x 35 T Millimeter Breite: 235 mm, Höhe: 155 mm, Tiefe: 35 mm
• Gewicht: 640 Gramm

Über diesen Titel

Inhaltsangabe

This book presents recent advances in quality measures in data mining.

Von der hinteren Coverseite

Data mining analyzes large amounts of data to discover knowledge relevant to decision making. Typically, numerous pieces of knowledge are extracted by a data mining system and presented to a human user, who may be a decision-maker or a data-analyst. The user is confronted with the task of selecting the pieces of knowledge that are of the highest quality or interest according to his or her preferences. Since this selection is sometimes a daunting task, designing quality and interestingness measures has become an important challenge for data mining researchers in the last decade.

This volume presents the state of the art concerning quality and interestingness measures for data mining. The book summarizes recent developments and presents original research on this topic. The chapters include surveys, comparative studies of existing measures, proposals of new measures, simulations, and case studies. Both theoretical and applied chapters are included. Papers for this book were selected and reviewed for correctness and completeness by an international review committee.