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Scientific data mining : a practical perspective / Chandrika Kamath.

By: Material type: TextTextPublication details: Philadelphia : Society for Industrial and Applied Mathematics, c2009.Description: xviii, 286 p. : ill. ; 26 cmISBN:
  • 9780898716757
Subject(s): LOC classification:
  • QA76.9.D343 K151 2009
Other classification:
  • ST 530
Online resources:
Contents:
Data mining in science and engineering -- Common themes in mining scientific data -- The scientific data mining process -- Reducing the size of the data -- Fusing different data modalities -- Enhancing image data -- Finding objects in the data -- Extracting features describing the objects -- Reducing the dimension of the data -- Finding patterns in the data -- Visualizing the data and validating the results -- Scientific data mining systems -- Lessons learned, challenges, and opportunities.
Summary: Chandrika Kamath describes how techniques from the multi-disciplinary field of data mining can be used to address the modern problem of data overload in science and engineering domains. Starting with a survey of analysis problems in different applications, it identifies the common themes across these domains.
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Item type Home library Collection Call number Copy number Status Date due Barcode
Open Shelf Books Open Shelf Books Al-Ghazali Library BKS QA76.9.D343 K151 2009 (Browse shelf(Opens below)) 1 Available GHAZ13042455

Includes bibliographical references (p. 235-277) and index.

Data mining in science and engineering -- Common themes in mining scientific data -- The scientific data mining process -- Reducing the size of the data -- Fusing different data modalities -- Enhancing image data -- Finding objects in the data -- Extracting features describing the objects -- Reducing the dimension of the data -- Finding patterns in the data -- Visualizing the data and validating the results -- Scientific data mining systems -- Lessons learned, challenges, and opportunities.

Chandrika Kamath describes how techniques from the multi-disciplinary field of data mining can be used to address the modern problem of data overload in science and engineering domains. Starting with a survey of analysis problems in different applications, it identifies the common themes across these domains.