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 / edited by Tru Cao, Ee-Peng Lim, Zhi-Hua Zhou, Tu-Bao Ho, David Cheung, Hiroshi Motoda.

Format:

Book

Contributor:

Cao, Tru, Editor.

Lim, Ee-Peng, Editor.

Zhou, Zhi-Hua., Editor.

Ho, Tu Bao, Editor.

Cheung, David, Editor.

Motoda, Hiroshi, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence 2945-9141 ; 9078

Lecture Notes in Artificial Intelligence, 2945-9141 ; 9078

Language:

English

Subjects (All):

Data mining.

Artificial intelligence.

Information storage and retrieval systems.

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Information Storage and Retrieval.

Local Subjects:

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Information Storage and Retrieval.

Physical Description:

1 online resource (XXIX, 773 pages) : 237 illustrations

Edition:

1st ed. 2015.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2015.

System Details:

text file PDF

Summary:

This two-volume set, LNAI 9077 + 9078, constitutes the refereed proceedings of the 19th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2015, held in Ho Chi Minh City, Vietnam, in May 2015. The proceedings contain 117 paper carefully reviewed and selected from 405 submissions. They have been organized in topical sections named: social networks and social media; classification; machine learning; applications; novel methods and algorithms; opinion mining and sentiment analysis; clustering; outlier and anomaly detection; mining uncertain and imprecise data; mining temporal and spatial data; feature extraction and selection; mining heterogeneous, high-dimensional, and sequential data; entity resolution and topic-modeling; itemset and high-performance data mining; and recommendations.

Contents:

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 and Semantic 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.

Other Format:

Printed edition:

ISBN:

978-3-319-18032-8

9783319180328

Access Restriction:

Restricted for use by site license.