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Titlebook: Local Pattern Detection; International Semina Katharina Morik,Jean-Fran?ois Boulicaut,Arno Siebe Conference proceedings 2005 Springer-Verla

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書目名稱Local Pattern Detection
副標(biāo)題International Semina
編輯Katharina Morik,Jean-Fran?ois Boulicaut,Arno Siebe
視頻videohttp://file.papertrans.cn/588/587692/587692.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Local Pattern Detection; International Semina Katharina Morik,Jean-Fran?ois Boulicaut,Arno Siebe Conference proceedings 2005 Springer-Verla
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
關(guān)鍵詞algorithmic learning; algorithms; calculus; data analysis; data mining; learning; pattern detection; patter
版次1
doihttps://doi.org/10.1007/b137601
isbn_softcover978-3-540-26543-6
isbn_ebook978-3-540-31894-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2005
The information of publication is updating

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Local Pattern Detection and Clustering,usly high data density, which represent real underlying phenomena. We discuss some aspects of this definition and examine the differences between clustering and pattern detection (if any), before we investigate how to utilize clustering algorithms for pattern detection. A modification of an existing
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Visualizing Very Large Graphs Using Clustering Neighborhoods,re is in the representation change to enable better handling of the data. The idea of the method consists from three major steps: (1) First, we transform a graph into a sparse matrix, where for each vertex in the graph there is one sparse vector in the matrix. Sparse vectors have non-zero components
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Features for Learning Local Patterns in Time-Stamped Data,stomers, machine parts,...) which is important for the business at hand. In contrast, the majority of objects obey well-known rules and is not of interest for the analysis. In terms of a classification task, the small group means that there are very few positive examples and within them, there is so
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Boolean Property Encoding for Local Set Pattern Discovery: An Application to Gene Expression Data Ation rules, closed sets) discovery techniques from boolean matrices that encode gene properties. Detecting local patterns by means of complete constraint-based mining techniques turns to be an important complementary approach or invaluable counterpart to heuristic global model mining. To take the mo
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