Scientific data mining :
Kamath, Chandrika.
Scientific data mining : a practical perspective / Chandrika Kamath. - Philadelphia : Society for Industrial and Applied Mathematics, c2009. - xviii, 286 p. : ill. ; 26 cm.
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.
9780898716757
2008056149
Data mining.
Science--Databases
Engineering--Databases.
QA76.9.D343 / K151 2009
Scientific data mining : a practical perspective / Chandrika Kamath. - Philadelphia : Society for Industrial and Applied Mathematics, c2009. - xviii, 286 p. : ill. ; 26 cm.
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.
9780898716757
2008056149
Data mining.
Science--Databases
Engineering--Databases.
QA76.9.D343 / K151 2009