Constraint-Based Mining and Inductive Databases [electronic resource] : European Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March 11-13, 2004, Revised Selected Papers / edited by Jean-François Boulicaut, Luc Raedt, Heikki Mannila.
Material type: TextSeries: Lecture Notes in Computer Science ; 3848Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006Description: X, 404 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540313519Subject(s): Computer science | Database management | Information storage and retrieval systems | Artificial intelligence | Optical pattern recognition | Computer Science | Artificial Intelligence (incl. Robotics) | Computation by Abstract Devices | Database Management | Information Storage and Retrieval | Pattern RecognitionAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 LOC classification: Q334-342TJ210.2-211.495Online resources: Click here to access onlineCurrent library | Home library | Call number | Materials specified | URL | Status | Date due | Barcode |
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IMSc Library | IMSc Library | Link to resource | Available | EBK3502 |
The Hows, Whys, and Whens of Constraints in Itemset and Rule Discovery -- A Relational Query Primitive for Constraint-Based Pattern Mining -- To See the Wood for the Trees: Mining Frequent Tree Patterns -- A Survey on Condensed Representations for Frequent Sets -- Adaptive Strategies for Mining the Positive Border of Interesting Patterns: Application to Inclusion Dependencies in Databases -- Computation of Mining Queries: An Algebraic Approach -- Inductive Queries on Polynomial Equations -- Mining Constrained Graphs: The Case of Workflow Systems -- CrossMine: Efficient Classification Across Multiple Database Relations -- Remarks on the Industrial Application of Inductive Database Technologies -- How to Quickly Find a Witness -- Relevancy in Constraint-Based Subgroup Discovery -- A Novel Incremental Approach to Association Rules Mining in Inductive Databases -- Employing Inductive Databases in Concrete Applications -- Contribution to Gene Expression Data Analysis by Means of Set Pattern Mining -- Boolean Formulas and Frequent Sets -- Generic Pattern Mining Via Data Mining Template Library -- Inductive Querying for Discovering Subgroups and Clusters.
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