| 书目名称 | Local Pattern Detection | | 副标题 | International Semina | | 编辑 | Katharina Morik,Jean-François Boulicaut,Arno Siebe | | 视频video | | | 概述 | Includes supplementary material: | | 丛书名称 | Lecture Notes in Computer Science | | 图书封面 |  | | 描述 | 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 | | 出版日期 | Conference proceedings 2005 | | 关键词 | algorithmic learning; algorithms; calculus; data analysis; data mining; learning; pattern detection; patter | | 版次 | 1 | | doi | https://doi.org/10.1007/b137601 | | isbn_softcover | 978-3-540-26543-6 | | isbn_ebook | 978-3-540-31894-1Series ISSN 0302-9743 Series E-ISSN 1611-3349 | | issn_series | 0302-9743 | | copyright | Springer-Verlag Berlin Heidelberg 2005 |
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发表于 2025-3-21 19:30:01
