2018 2nd International Conference on Compute and Data Analysis (ICCDA 2018)

Foundations

Mathematical, probabilistic and statistical models and theories

Machine learning theories, models and systems

Knowledge discovery theories, models and systems

Manifold and metric learning

Deep learning

Scalable analysis and learning

Non-iidness learning

Heterogeneous data/information integration

Data pre-processing, sampling and reduction

Dimensionality reduction

Feature selection, transformation and construction

Large scale optimization

High performance computing for data analytics

Architecture, management and process for data science

Data analytics, machine learning and knowledge discovery

Learning for streaming data

Learning for structured and relational data

Latent semantics and insight learning

Mining multi-source and mixed-source information

Mixed-type and structure data analytics

Cross-media data analytics

Big data visualization, modeling and analytics

Multimedia/stream/text/visual analytics

Relation, coupling, link and graph mining

Personalization analytics and learning

Web/online/social/network mining and learning

Structure/group/community/network mining

Cloud computing and service data analysis

Storage, retrieval and search

Data warehouses, cloud architectures

Large-scale databases

Information and knowledge retrieval, and semantic search

Web/social/databases query and search

Personalized search and recommendation

Human-machine interaction and interfaces

Crowdsourcing and collective intelligence