ICDMA'13 2013 - 3rd International Journal conference on Data Mining and Its Applications (ICDMA'13)

Regression/Classification
Time series forecasting
Segmentation/Clustering/Association
Deviation and outlier detection
Explorative and visual data mining
Web mining
Mining text and semi-structured data
Temporal and spatial data mining
Multimedia mining (audio/video)
Mining large scale data
Distributed and grid based data mining
Data and knowledge representation
Data warehousing and OLAP integration
Integration of prior/domain knowledge
Metadata and ontologies
Agent technologies for data mining
Legal and social aspects of data mining
Data Mining Process
Data cleaning and preparation
Feature selection and transformation
Attribute discretisation and encoding
Sampling and rebalancing
Missing value imputation
Model selection/assessment and comparison
Induction principles
Model interpretation
Data Mining Applications
Data Mining for Business Intelligence
Emerging technologies in data mining
Computational performance issues in data mining
Data mining in usability
Advanced prediction modelling using data mining
Data mining and national security
Data mining tools
Data analysis
Data preparation techniques (selection, transformation, and preprocessing)
Information extraction methodologies
Clustering algorithms used in data mining
Genetic algorithms and categorization techniques used in data mining
Data and information integration
Microarray design and analysis
Privacy-preserving data mining
Active data mining
Statistical methods used in data mining
Multidimensional data
Case studies and prototypes
Automatic data cleaning
Data visualization
Theory and practice - knowledge representation and discovery
Knowledge Discovery in Databases (KDD)
Uncertainty management
Data reduction methods
Data engineering
Content mining
Indexing schemes
Information retrieval
Metadata use and management
Multidimensional query languages and query optimization
Multimedia information systems
Search engine query processing
Pattern mining
Applications (examples: data mining in education, marketing, finance and financial services, business applications, medicine, bioinformatics, biological sciences, science and technology, industry and government, ...)
Data Mining Algorithms
Artificial neural networks
Fuzzy logic and rough sets
Decision trees/rule learners
Support vector machines
Evolutionary computation/meta heuristics
Statistical methods
Collaborative filtering
Case based reasoning
Link and sequence analysis
Ensembles/committee approaches