ICMLDA 2014 - International Conference on Machine Learning and Data Analysis (ICMLDA'14)

The topics of the ICMLDA'14 include, but not limited to, the following:
Machine Learning:
artificial neural networks
Bayesian networks
case-based reasoning
computational models of human learning
computational learning theory
cooperative learning
decision tree
discovery of scientific laws
evolutionary computation
statistical relational learning
grammatical inference
incremental induction and on-line learning
inductive logic programming
information retrieval and learning
instance based learning
kernel methods
knowledge acquisition and learning
knowledge base refinement
knowledge intensive learning
learning from text and web
evaluation metrics and methodologies
machine learning of natural language
meta learning
multi-agent learning
multi-strategy learning
planning and learning
reinforcement learning
revision and restructuring
statistical approaches
unsupervised learning
vision and learning
Data analysis and databases
database integration
inductive databases
data mining query languages
data mining query optimization
Foundations of data analysis
complexity issues
knowledge (pattern) representation
global vs. local patterns
logic for data mining
statistical inference and probabilistic modelling
Data pre-processing
dimensionality reduction
data reduction
discretization
uncertain and missing information handling
Innovative applications
mining bio-medical data
web content, structure and usage mining
semantic web mining
mining governmental data, mining for the public administration
personalization
adaptive data mining architectures
invisible data mining
Algorithms and techniques:
classification
clustering
frequent patterns
rule discovery
statistical techniques and mixture models
constraint-based mining
incremental algorithms
scalable algorithms
distributed and parallel algorithms
privacy preserving data mining
multi-relational data mining
KDD process and process-centric data analysis
models of the KDD process
standards for the KDD process
background knowledge integration
collaborative data mining
vertical data mining environments
Analysis of different forms of data
graph, tree, sequence mining
semi-structured and XML data mining
text mining
temporal, spatial, and spatio-temporal data mining
data stream mining
multimedia miningPattern post-processing
Pattern post-processing
quality assessment
visualization
knowledge interpretation and use