Advances in Knowledge Discovery and Data Mining - neues Buch

EAN (ISBN-13): 9783319180328
Erscheinungsjahr: 2015
Herausgeber: Springer-Verlag GmbH

Buch in der Datenbank seit 2015-12-02T17:38:37+01:00 (Zurich)
Detailseite zuletzt geändert am 2023-02-19T18:38:25+01:00 (Zurich)
ISBN/EAN: 9783319180328

ISBN - alternative Schreibweisen:
978-3-319-18032-8
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: peng, lim, hua, cheung, jan heitmann
Titel des Buches: data mining, chi minh, knowledge discovery, pacific discovery

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Daten vom Verlag:

Autor/in: Tru Cao; Ee-Peng Lim; Zhi-Hua Zhou; Tu-Bao Ho; David Cheung; Hiroshi Motoda
Titel: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence; Advances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II
Verlag: Springer; Springer International Publishing
773 Seiten
Erscheinungsjahr: 2015-05-08
Cham; CH
Sprache: Englisch
53,49 € (DE)
55,00 € (AT)
59,00 CHF (CH)
Available
XXIX, 773 p. 237 illus.

EA; E107; eBook; Nonbooks, PBS / Informatik, EDV/Informatik; Data Mining; Verstehen; classification; clustering; collaborative computing; data streams; factorization methods; information retrieval; kernel methods; learning paradigms; machine learning; machine learning algorithms; probabilistic reasoning; recommender systems; retrieval tasks and goals; sentiment analysis; social computing; social networks; supervised learning; temporal data; uncertainty; unsupervised learning; C; Data Mining and Knowledge Discovery; Artificial Intelligence; Information Storage and Retrieval; Computer Science; Wissensbasierte Systeme, Expertensysteme; Künstliche Intelligenz; Informationsrückgewinnung, Information Retrieval; Data Warehousing; BC

Opinion Mining and Sentiment Analysis.- Emotion Cause Detection for Chinese Micro-Blogs Based on ECOCC Model.- Parallel Recursive Deep Model for Sentiment Analysis.- Sentiment Analysis in Transcribed Utterances.- Rating Entities and Aspects Using a Hierarchical Model.- Sentiment Analysis on Microblogging by Integrating Text and Image Features.- TSum4act: A Framework for Retrieving and Summarizing Actionable Tweets during a Disaster for Reaction.- Clustering.- Evolving Chinese Restaurant Processes for Modeling Evolutionary Traces in Temporal Data.- Small-Variance Asymptotics for Bayesian Nonparametric Models with Constraints.- Spectral Clustering for Large-Scale Social Networks via a Pre-Coarsening Sampling Based Nyström Method.- pcStream: A Stream Clustering Algorithm for Dynamically Detecting and Managing Temporal Contexts.- Clustering Over Data Streams Based on Growing Neural Gas.- Computing and Mining ClustCube Cubes Efficiently.- Outlier and Anomaly Detection Contextual Anomaly Detection Using Log-Linear Tensor Factorization.- A Semi-Supervised Framework for Social Spammer Detection.- Fast One-Class Support Vector Machine for Novelty Detection.- ND-SYNC: Detecting Synchronized Fraud Activities.- An Embedding Scheme for Detecting Anomalous Block Structured Graphs.- A Core-Attach Based Method for Identifying Protein Complexes in Dynamic PPI Networks.- Mining Uncertain and Imprecise Data Mining Uncertain Sequential Patterns in Iterative MapReduce.- Quality Control for Crowdsourced POI Collection.- Towards Efficient Sequential Pattern Mining in Temporal Uncertain Databases.- Preference-Based Top-k Representative Skyline Queries on Uncertain Databases.- Cluster Sequence Mining: Causal Inference with Time and Space Proximity under Uncertainty.- Achieving Accuracy Guarantee for Answering Batch Queries with Differential Privacy.- Mining Temporal and Spatial Data Automated Classification of Passing in Football.- Stabilizing Sparse Cox Model Using Statistic andSemantic Structures in Electronic Medical Records.- Predicting Next Locations with Object Clustering and Trajectory Clustering.- A Plane Moving Average Algorithm for Short-Term Traffic Flow Prediction.- Recommending Profitable Taxi Travel Routes Based on Big Taxi Trajectories Data.- Semi Supervised Adaptive Framework for Classifying Evolving Data Stream.- Feature Extraction and Selection Cost-Sensitive Feature Selection on Heterogeneous Data.- A Feature Extraction Method for Multivariate Time Series Classification Using Temporal Patterns.- Scalable Outlying-Inlying Aspects Discovery via Feature Ranking.- A DC Programming Approach for Sparse Optimal Scoring.- Graph Based Relational Features for Collective Classification.- A New Feature Sampling Method in Random Forests for Predicting High-Dimensional Data.- Mining Heterogeneous, High Dimensional, and Sequential Data Seamlessly Integrating Effective Links with Attributes for Networked Data Classification.- Clustering on Multi-source Incomplete Data via Tensor Modeling and Factorization.- Locally Optimized Hashing for Nearest Neighbor Search.- Do-Rank: DCG Optimization for Learning-to-Rank in Tag-Based Item Recommendation Systems.- Efficient Discovery of Recurrent Routine Behaviours in Smart Meter Time Series by Growing Subsequences.- Convolutional Nonlinear Neighbourhood Components Analysis for Time Series Classification.- Entity Resolution and Topic Modelling Clustering-Based Scalable Indexing for Multi-party Privacy-Preserving Record Linkage.- Efficient Interactive Training Selection for Large-Scale Entity Resolution.- Unsupervised Blocking Key Selection for Real-Time Entity Resolution.- Incorporating Probabilistic Knowledge into Topic Models.- Learning Focused Hierarchical Topic Models with Semi-Supervision in Microblogs.- Predicting Future Links Between Disjoint Research Areas Using Heterogeneous Bibliographic Information Network.- Itemset and High Performance Data Mining CPT+: Decreasing the Time/Space Complexity of the Compact Prediction Tree.- Mining Association Rules in Graphs Based on Frequent Cohesive Itemsets.- Mining High Utility Itemsets in Big Data.- Decomposition Based SAT Encodings for Itemset Mining Problems.- A Comparative Study on Parallel LDA Algorithms in MapReduce Framework.- Distributed Newton Methods for Regularized Logistic Regression.- Recommendation.- Coupled Matrix Factorization Within Non-IID Context.- Complementary Usage of Tips and Reviews for Location Recommendation in Yelp.- Coupling Multiple Views of Relations for Recommendation.- Pairwise One Class Recommendation Algorithm.- RIT: Enhancing Recommendation with Inferred Trust.