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Advanced Data Mining and Applications: 9th International Conference, ADMA 2013, Hangzhou, China, December 14-16, 2013, Proceedings, Part II de Hiroshi Motoda

Advanced Data Mining and Applications: 9th International Conference, ADMA 2013, Hangzhou, China, December 14-16, 2013, Proceedings, Part II: Lecture Notes in Computer Science, cartea 8347

Editat de Hiroshi Motoda, Zhaohui Wu, Longbing Cao, Osmar Zaiane, Min Yao, Wei Wang
en Limba Engleză Paperback – 7 ian 2014
The two-volume set LNAI 8346 and 8347 constitutes the thoroughly refereed proceedings of the 9th International Conference on Advanced Data Mining and Applications, ADMA 2013, held in Hangzhou, China, in December 2013.
The 32 regular papers and 64 short papers presented in these two volumes were carefully reviewed and selected from 222 submissions. The papers included in these two volumes cover the following topics: opinion mining, behavior mining, data stream mining, sequential data mining, web mining, image mining, text mining, social network mining, classification, clustering, association rule mining, pattern mining, regression, predication, feature extraction, identification, privacy preservation, applications, and machine learning.
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Specificații

ISBN-13: 9783642539169
ISBN-10: 3642539165
Pagini: 560
Ilustrații: XXII, 538 p. 163 illus.
Dimensiuni: 155 x 235 x 35 mm
Greutate: 0.78 kg
Ediția:2013
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

Clustering.- Semi-Supervised Clustering Ensemble Evolved by Genetic Algorithm for Web Video Categorization.- A Scalable Approach for General Correlation Clustering.- A fast spectral clustering method based on growing vector quantization for large data sets.- A Novel Deterministic Sampling Technique to Speedup Clustering Algorithms.- Software Clustering using Automated Feature Subset Selection.- The Use of Transfer Algorithm for Clustering Categorical Data.- eDARA: Ensembles DARA.- Efficient mining maximal variant and low usage rate biclusters without candidate maintenance in real function-resource matrix: the DeCluster algorithm.- Association Rule Mining.- MEIT: Memory Efficient Itemset Tree for Targeted Association Rule Mining.- Pattern Mining.- Mining Frequent Patterns in Print Logs with Semantically Alternative Labels.- Minimising K-Dominating Set in Arbitrary Network Graphs.- Regression.- Logistic Regression Bias Correction for Large Scale Data with Rare Events.- An Automatical Moderating System for FML using Hashing Regression.- Batch-to-Batch Iterative Learning Control Based on Kernel Independent Component Regression Model.- Prediction.- Deep Architecture for Traffic Flow Prediction.- Compact Prediction Tree: A Lossless Model for Accurate Sequence Prediction.- Generalization of Malaria Incidence Prediction Models by Correcting Sample Selection Bias.- Protein Interaction Hot Spots Prediction using LS-SVM within the Bayesian Interpretation.- Predicting the survival status of cancer patients with Traditional Chinese Medicine Symptom Variation using Logistic Regression Model.- Feature Extraction.- Exploiting Multiple Features for Learning to Rank in Expert Finding.- Convolution Neural Network for Relation Extraction.- Extracting Fuzzy Rules from Hierarchical Heterogeneous Neural Networks for Cardiovascular Diseases Diagnosis.- kDMI: A Novel Method for Missing Values Imputation Using Two Levels of Horizontal Partitioning in a Data set.- Identification.- Traffic Session Identification based on Statistical Language Model.- Role Identification Based on the Information Dependency Complexity.- Detecting Professional Spam Reviewers.- Chinese Comparative Sentence Identification Based on the Combination of Rules and Statistics.- Privacy Preservation.- Utility Enhancement for Privacy Preserving Health Data Publishing.- Optimizing Placement of Mix Zones to Preserve Users' Privacy for Continuous Query Services in Road Networks.- Applications.- Comparison of Cutoff Strategies for Geometrical Features in Machine Learning-based Scoring Functions.- Bichromatic Reverse Ranking Query in Two Dimensions.- Passive Aggressive Algorithm for Online Portfolio Selection with Piecewise Loss Function.- Mining Item Popularity for Recommender Systems.- Exploring an Ichthyoplankton Database from a Freshwater Reservoir in Legal Amazon.- A Pre-initialization Stage of Population-based Bio-inspired Metaheuristics for Handling Expensive Optimization Problems.- A Hybrid-sorting Semantic Matching Method.- Improving Few Occurrence Feature Performance in Distant Supervision for Relation Extraction.- Cluster Labeling Extraction and Ranking Feature Selection for High Quality XML Pseudo Relevance Feedback Fragments Set.- Informed Weighted Random Projection for Dimension Reduction.- Protocol Specification Inference Based on Keywords Identification.- An Adaptive Collaborative Filtering Algorithm Based on Multiple Features.- Machine Learning.- Ensemble of Unsupervised and Supervised Models with Different Label Spaces.- Cost-sensitive Extreme Learning Machine.- Multi-objective Optimization for Overlapping Community Detection.- Endmember Extraction by Exemplar Finder.- EEG-Based User Authentication in Multilevel Security Systems.- A new fuzzy extreme learning machine for regression problems with outliers or noises.

Textul de pe ultima copertă

The two-volume set LNAI 8346 and 8347 constitutes the thoroughly refereed proceedings of the 9th International Conference on Advanced Data Mining and Applications, ADMA 2013, held in Hangzhou, China, in December 2013.
The 32 regular papers and 64 short papers presented in these two volumes were carefully reviewed and selected from 222 submissions. The papers included in these two volumes cover the following topics: opinion mining, behavior mining, data stream mining, sequential data mining, web mining, image mining, text mining, social network mining, classification, clustering, association rule mining, pattern mining, regression, predication, feature extraction, identification, privacy preservation, applications, and machine learning.