Knowledge Discovery in Inductive Databases [electronic resource] : 5th International Workshop, KDID 2006 Berlin, Germany, September 18, 2006 Revised Selected and Invited Papers / edited by Sašo Džeroski, Jan Struyf.
Material type: TextSeries: Lecture Notes in Computer Science ; 4747Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2007Description: X, 301 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540755494Subject(s): Computer science | Database management | Artificial intelligence | Computer Science | Database Management | Artificial Intelligence (incl. Robotics)Additional physical formats: Printed edition:: No titleDDC classification: 005.74 LOC classification: QA76.9.D3Online 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 | EBK7874 |
Invited Talk -- Value, Cost, and Sharing: Open Issues in Constrained Clustering -- Contributed Papers -- Mining Bi-sets in Numerical Data -- Extending the Soft Constraint Based Mining Paradigm -- On Interactive Pattern Mining from Relational Databases -- Analysis of Time Series Data with Predictive Clustering Trees -- Integrating Decision Tree Learning into Inductive Databases -- Using a Reinforced Concept Lattice to Incrementally Mine Association Rules from Closed Itemsets -- An Integrated Multi-task Inductive Database VINLEN: Initial Implementation and Early Results -- Beam Search Induction and Similarity Constraints for Predictive Clustering Trees -- Frequent Pattern Mining and Knowledge Indexing Based on Zero-Suppressed BDDs -- Extracting Trees of Quantitative Serial Episodes -- IQL: A Proposal for an Inductive Query Language -- Mining Correct Properties in Incomplete Databases -- Efficient Mining Under Rich Constraints Derived from Various Datasets -- Three Strategies for Concurrent Processing of Frequent Itemset Queries Using FP-Growth -- Discussion Paper -- Towards a General Framework for Data Mining.
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