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EBOOK
Conference ADMA (Conference) (7th : 2011 : Beijing, China)
Title Advanced data mining and applications : 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, proceedings. Part II / Jie Tang [and others] (eds.).
Imprint Berlin ; New York : Springer, 2011.

LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
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Series Lecture notes in computer science, 0302-9743
Lecture notes in artificial intelligence ; 7121
LNCS sublibrary. SL 7, Artificial intelligence
Lecture notes in computer science.
Lecture notes in computer science. Lecture notes in artificial intelligence ; 7121.
LNCS sublibrary. SL 7, Artificial intelligence.
Subject Data mining -- Congresses.
Computer science
Computer software
Database management
Data mining
Information storage and retrieval systems
Artificial intelligence
Information Storage and Retrieval
Data Mining and Knowledge Discovery
Alt Name Tang, Jie (Computer scientist)
LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
View online
Series Lecture notes in computer science, 0302-9743
Lecture notes in artificial intelligence ; 7121
LNCS sublibrary. SL 7, Artificial intelligence
Lecture notes in computer science.
Lecture notes in computer science. Lecture notes in artificial intelligence ; 7121.
LNCS sublibrary. SL 7, Artificial intelligence.
Subject Data mining -- Congresses.
Computer science
Computer software
Database management
Data mining
Information storage and retrieval systems
Artificial intelligence
Information Storage and Retrieval
Data Mining and Knowledge Discovery
Alt Name Tang, Jie (Computer scientist)
Description 1 online resource (xvii, 419 pages).
Bibliography Note Includes bibliographical references and author index.
Summary Annotation This volume constitutes the refereed proceedings of the 7th International Conference on Advanced Data Mining and Applications, ADMA 2011, held in Beijing, China, in December 2011.
Contents Intro; Title page; Preface; Organization; Table of Contents; Generating Syntactic Tree Templates for Feature-Based Opinion Mining; Introduction; Related Work; The Approach; Syntactic Tree Template; Building Syntactic Tree Template Lexicon; Similarity Computation; Lexicon Generation; Experiments and Results; Corpus; Preprocessing Results; Appraisal Expressions Extraction Experiments; Conclusions and Future Work; References; Handling Concept Drift via Ensemble and Class Distribution Estimation Technique; Introduction; Class Distributions Estimation-Based Methods (CDE-Based Methods)
Cost Sensitive Classifier (CSC)Class Distribution; Positive-to-Negative Ratio (pr/nr); Distribution Mismatch Ratio (DMR); Class Distribution Estimation Based Method (CDE); CDE Oracle Method; Our Methods; CDE-EM-AVG-N; CDE-EM-BM-N; CDE-EM-AVGBM-M-N; CDE-EM-EX-N; Experiment Methodology; Experiment Results; Conclusion; References; HUE-Stream: Evolution-Based Clustering Technique for Heterogeneous Data Streams with Uncertainty; Introduction; Basic Concepts of Evolution-Based Stream Clustering with Uncertainty; Tuple-Level and Dimension-Level Uncertainty
Cluster Representation Using Fading Cluster Structure with HistogramDistance Functions; Evolution-Based Stream Clustering; The Algorithm; Overview of HUE-Stream Algorithm; Experimental Results; Effectiveness Test; Sensitivity Test; Efficiency Test; Conclusions; References; Hybrid Artificial Immune Algorithm and CMAC Neural Network Classifier for Supporting Business and Medical Decision Making; Introduction; Related Works; MIMO CMAC NN Classifier; Artificial Immune Algorithm; Method; Results; Australian Credit Approval Credit Dataset; Diabetes Dataset; Conclusions; References
Improving Suffix Tree Clustering with New Ranking and Similarity MeasuresIntroduction; Related Work; STHAC: Suffix Tree Hierarchical Agglomerative Clustering; Step 1 -- Document Cleaning; Step 2 -- Base Cluster Identification; Step 3 -- Clusters Ranking and Filtering; Step 4 -- Merging Clusters; Step 5 -- Cluster Cleaning; Evaluation; Comparison STHAC with STC and Other Algorithms; Experimental Results; Further Experiments; Conclusions; References; Individual Doctor Recommendation Model on Medical Social Network; Introduction; The Method; Doctor-Patient Relationships Mining via TPFG
Features Fxtraction for Calculating AD-CDsAuthority Degrees Sorting via Ranking SVM; Individual Doctor Recommendation via Weighted Average Method; Experiments and Evaluations; Mining Patient-Doctor Relationships via TPFG; Effectiveness of Ranking-SVM-Based Algorithm; AD-CDs Sorting Evaluation; Recommendation Performance; Related Work; Conclusions; References; Influence Maximizing and Local Influenced Community Detection Based on Multiple Spread Model; Introduction; Related Works; Independent Cascade Model (ICM); Local Community Detection; Multiple Spread Model (MSM); Influence Maximizing
ISBN 9783642258565 (electronic bk.)
3642258565 (electronic bk.)
9783642258558
ISBN/ISSN 10.1007/978-3-642-25856-5
OCLC # 769134692
Additional Format Printed edition: 9783642258558