000 | 05241nam a22005895i 4500 | ||
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001 | 978-3-540-45681-0 | ||
003 | DE-He213 | ||
005 | 20160624102006.0 | ||
007 | cr nn 008mamaa | ||
008 | 121227s2002 gw | s |||| 0|eng d | ||
020 |
_a9783540456810 _9978-3-540-45681-0 |
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024 | 7 |
_a10.1007/3-540-45681-3 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTJ210.2-211.495 | |
072 | 7 |
_aUYQ _2bicssc |
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072 | 7 |
_aTJFM1 _2bicssc |
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072 | 7 |
_aCOM004000 _2bisacsh |
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082 | 0 | 4 |
_a006.3 _223 |
245 | 1 | 0 |
_aPrinciples of Data Mining and Knowledge Discovery _h[electronic resource] : _b6th European Conference, PKDD 2002 Helsinki, Finland, August 19–23, 2002 Proceedings / _cedited by Tapio Elomaa, Heikki Mannila, Hannu Toivonen. |
260 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2002. |
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264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2002. |
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300 |
_aXIV, 514 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aLecture Notes in Computer Science, Lecture Notes in Artificial Intelligence, _x0302-9743 ; _v2431 |
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505 | 0 | _aContributed Papers -- Optimized Substructure Discovery for Semi-structured Data -- Fast Outlier Detection in High Dimensional Spaces -- Data Mining in Schizophrenia Research — Preliminary Analysis -- Fast Algorithms for Mining Emerging Patterns -- On the Discovery of Weak Periodicities in Large Time Series -- The Need for Low Bias Algorithms in Classification Learning from Large Data Sets -- Mining All Non-derivable Frequent Itemsets -- Iterative Data Squashing for Boosting Based on a Distribution-Sensitive Distance -- Finding Association Rules with Some Very Frequent Attributes -- Unsupervised Learning: Self-aggregation in Scaled Principal Component Space* -- A Classification Approach for Prediction of Target Events in Temporal Sequences -- Privacy-Oriented Data Mining by Proof Checking -- Choose Your Words Carefully: An Empirical Study of Feature Selection Metrics for Text Classification -- Generating Actionable Knowledge by Expert-Guided Subgroup Discovery -- Clustering Transactional Data -- Multiscale Comparison of Temporal Patterns in Time-Series Medical Databases -- Association Rules for Expressing Gradual Dependencies -- Support Approximations Using Bonferroni-Type Inequalities -- Using Condensed Representations for Interactive Association Rule Mining -- Predicting Rare Classes: Comparing Two-Phase Rule Induction to Cost-Sensitive Boosting -- Dependency Detection in MobiMine and Random Matrices -- Long-Term Learning for Web Search Engines -- Spatial Subgroup Mining Integrated in an Object-Relational Spatial Database -- Involving Aggregate Functions in Multi-relational Search -- Information Extraction in Structured Documents Using Tree Automata Induction -- Algebraic Techniques for Analysis of Large Discrete-Valued Datasets -- Geography of Di.erences between Two Classes of Data -- Rule Induction for Classification of Gene Expression Array Data -- Clustering Ontology-Based Metadata in the Semantic Web -- Iteratively Selecting Feature Subsets for Mining from High-Dimensional Databases -- SVM Classification Using Sequences of Phonemes and Syllables -- A Novel Web Text Mining Method Using the Discrete Cosine Transform -- A Scalable Constant-Memory Sampling Algorithm for Pattern Discovery in Large Databases -- Answering the Most Correlated N Association Rules Efficiently -- Mining Hierarchical Decision Rules from Clinical Databases Using Rough Sets and Medical Diagnostic Model -- Efficiently Mining Approximate Models of Associations in Evolving Databases -- Explaining Predictions from a Neural Network Ensemble One at a Time -- Structuring Domain-Specific Text Archives by Deriving a Probabilistic XML DTD -- Separability Index in Supervised Learning -- Invited Papers -- Finding Hidden Factors Using Independent Component Analysis -- Reasoning with Classifiers* -- A Kernel Approach for Learning from Almost Orthogonal Patterns -- Learning with Mixture Models: Concepts and Applications. | |
650 | 0 | _aComputer science. | |
650 | 0 | _aDatabase management. | |
650 | 0 | _aInformation storage and retrieval systems. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aText processing (Computer science. | |
650 | 1 | 4 | _aComputer Science. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
650 | 2 | 4 | _aDatabase Management. |
650 | 2 | 4 | _aMathematical Logic and Formal Languages. |
650 | 2 | 4 | _aProbability and Statistics in Computer Science. |
650 | 2 | 4 | _aDocument Preparation and Text Processing. |
650 | 2 | 4 | _aInformation Storage and Retrieval. |
700 | 1 |
_aElomaa, Tapio. _eeditor. |
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700 | 1 |
_aMannila, Heikki. _eeditor. |
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700 | 1 |
_aToivonen, Hannu. _eeditor. |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783540440376 |
786 | _dSpringer | ||
830 | 0 |
_aLecture Notes in Computer Science, Lecture Notes in Artificial Intelligence, _x0302-9743 ; _v2431 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/3-540-45681-3 |
942 |
_2EBK5515 _cEBK |
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999 |
_c34809 _d34809 |