2005, ISBN: 3540265430

[EAN: 9783540265436], Neubuch, [SC: 0.0], [PU: Springer Berlin Heidelberg], ALGORITHMUS; MUSTERERKENNUNG; ALGORITHMICLEARNING; ALGORITHMS; CALCULUS; DATAANALYSIS; DATAMINING; LEARNING; PA… Mehr…

[EAN: 9783540265436], Neubuch, [SC: 0.0], [PU: Springer Berlin Heidelberg], ALGORITHMUS; MUSTERERKENNUNG; ALGORITHMICLEARNING; ALGORITHMS; CALCULUS; DATAANALYSIS; DATAMINING; LEARNING; PATTERNDETECTION; PATTERNDISCOVERY; PATTERNMINING; PATTERNSEARCHES; TEXTMINING; USAGEPATTERNDETECTION; ALGORITHMANALYSISANDPROBLEMCOMPLEXITY, Druck auf Anfrage Neuware - Printed after ordering - Introduction The dramatic increase in available computer storage capacity over the last 10 years has led to the creation of very large databases of scienti c and commercial information. The need to analyze these masses of data has led to the evolution of the new eld knowledge discovery in databases (KDD) at the intersection of machine learning, statistics and database technology. Being interdisciplinary by nature, the eld o ers the opportunity to combine the expertise of di erent elds intoacommonobjective.Moreover,withineach elddiversemethodshave been developed and justi ed with respect to di erent quality criteria. We have toinvestigatehowthesemethods cancontributeto solvingthe problemofKDD. Traditionally, KDD was seeking to nd global models for the data that - plain most of the instances of the database and describe the general structure of the data. Examples are statistical time series models, cluster models, logic programs with high coverageor classi cation models like decision trees or linear decision functions. In practice, though, the use of these models often is very l- ited, because global models tend to nd only the obvious patterns in the data, 1 which domain experts already are aware of . What is really of interest to the users are the local patterns that deviate from the already-known background knowledge. David Hand, who organized a workshop in 2002, proposed the new eld of local patterns., Books<

2005, ISBN: 3540265430

[EAN: 9783540265436], Neubuch, [PU: Springer Berlin Heidelberg Jul 2005], ALGORITHMUS; MUSTERERKENNUNG; ALGORITHMICLEARNING; ALGORITHMS; CALCULUS; DATAANALYSIS; DATAMINING; LEARNING; PATT… Mehr…

[EAN: 9783540265436], Neubuch, [PU: Springer Berlin Heidelberg Jul 2005], ALGORITHMUS; MUSTERERKENNUNG; ALGORITHMICLEARNING; ALGORITHMS; CALCULUS; DATAANALYSIS; DATAMINING; LEARNING; PATTERNDETECTION; PATTERNDISCOVERY; PATTERNMINING; PATTERNSEARCHES; TEXTMINING; USAGEPATTERNDETECTION; ALGORITHMANALYSISANDPROBLEMCOMPLEXITY, This item is printed on demand - it takes 3-4 days longer - Neuware -Introduction The dramatic increase in available computer storage capacity over the last 10 years has led to the creation of very large databases of scienti c and commercial information. The need to analyze these masses of data has led to the evolution of the new eld knowledge discovery in databases (KDD) at the intersection of machine learning, statistics and database technology. Being interdisciplinary by nature, the eld o ers the opportunity to combine the expertise of di erent elds intoacommonobjective.Moreover,withineach elddiversemethodshave been developed and justi ed with respect to di erent quality criteria. We have toinvestigatehowthesemethods cancontributeto solvingthe problemofKDD. Traditionally, KDD was seeking to nd global models for the data that - plain most of the instances of the database and describe the general structure of the data. Examples are statistical time series models, cluster models, logic programs with high coverageor classi cation models like decision trees or linear decision functions. In practice, though, the use of these models often is very l- ited, because global models tend to nd only the obvious patterns in the data, 1 which domain experts already are aware of . What is really of interest to the users are the local patterns that deviate from the already-known background knowledge. David Hand, who organized a workshop in 2002, proposed the new eld of local patterns. 248 pp. Englisch, Books<

2014, ISBN: 9783540265436

[ED: Taschenbuch], [PU: Springer Berlin Heidelberg], Neuware - Introduction The dramatic increase in available computer storage capacity over the last 10 years has led to the creation of … Mehr…

[ED: Taschenbuch], [PU: Springer Berlin Heidelberg], Neuware - Introduction The dramatic increase in available computer storage capacity over the last 10 years has led to the creation of very large databases of scienti c and commercial information. The need to analyze these masses of data has led to the evolution of the new eld knowledge discovery in databases (KDD) at the intersection of machine learning, statistics and database technology. Being interdisciplinary by nature, the eld o ers the opportunity to combine the expertise of di erent elds intoacommonobjective.Moreover,withineach elddiversemethodshave been developed and justi ed with respect to di erent quality criteria. We have toinvestigatehowthesemethods cancontributeto solvingthe problemofKDD. Traditionally, KDD was seeking to nd global models for the data that - plain most of the instances of the database and describe the general structure of the data. Examples are statistical time series models, cluster models, logic programs with high coverageor classi cation models like decision trees or linear decision functions. In practice, though, the use of these models often is very l- ited, because global models tend to nd only the obvious patterns in the data, 1 which domain experts already are aware of . What is really of interest to the users are the local patterns that deviate from the already-known background knowledge. David Hand, who organized a workshop in 2002, proposed the new eld of local patterns., DE, [SC: 0.00], Neuware, gewerbliches Angebot, 235x155x14 mm, 248, [GW: 382g], Banküberweisung, PayPal, Internationaler Versand<

2002, ISBN: 9783540265436

Introduction The dramatic increase in available computer storage capacity over the last 10 years has led to the creation of very large databases of scienti?c and commercial information. T… Mehr…

Introduction The dramatic increase in available computer storage capacity over the last 10 years has led to the creation of very large databases of scienti?c and commercial information. The need to analyze these masses of data has led to the evolution of the new ?eld knowledge discovery in databases (KDD) at the intersection of machine learning, statistics and database technology. Being interdisciplinary by nature, the ?eld o?ers the opportunity to combine the expertise of di?erent ?elds intoacommonobjective.Moreover,withineach?elddiversemethodshave been developed and justi?ed with respect to di?erent quality criteria. We have toinvestigatehowthesemethods cancontributeto solvingthe problemofKDD. Traditionally, KDD was seeking to ?nd global models for the data that - plain most of the instances of the database and describe the general structure of the data. Examples are statistical time series models, cluster models, logic programs with high coverageor classi?cation models like decision trees or linear decision functions. In practice, though, the use of these models often is very l- ited, because global models tend to ?nd only the obvious patterns in the data, 1 which domain experts already are aware of . What is really of interest to the users are the local patterns that deviate from the already-known background knowledge. David Hand, who organized a workshop in 2002, proposed the new ?eld of local patterns., Springer<