You are invited to submit a paper for consideration. All accepted papers will be published in printed conference books/proceedings (ISBN) and will also be made available online. Like prior years, extended versions of selected papers will appear in journals and edited research books (publishers include: Springer, Elsevier, BMC, and others). In addition to the above, we have arranged two new book series; one with Elsevier publishers (Emerging Trends in Computer Science and Applied Computing) and another with Springer publishers (Transactions of Computational Science and Computational Intelligence). After the conference, a significant number of authors of accepted papers of our congress, will be given the opportunity to submit the extended version of their papers for publication consideration in these books. Each book in each series will be subject to Elsevier and Springer science indexing products (which includes: Scopus, Ei village, SCI, ...). To get a feeling about the conference's atmosphere, see some delegates photos available at:
http://worldcomp.phanfare.com/
http://infinitydempsey.smugmug.com/WorldComp

DMIN'15 is composed of a number of tracks, including: tutorials, sessions, workshops, posters, and panel discussions. The conference will be held July 27-30, 2015, Las Vegas, USA.
 
SCOPE: Submitted papers should be related to Data Mining, Data Science, Machine Learning and similar topics.

Topics of interest include, but are not limited to, the following:

 
Data Mining/Machine Learning Tasks
    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 "big data"
    Others

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
    Others

Data Mining Integration
    Mining large scale data/big 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
    Others

Data Mining Applications
    Bioinformatics
    Medicine Data Mining
    Business/Corporate/Industrial Data Mining
    Credit Scoring
    Direct Marketing
    Database Marketing
    Engineering Mining
    Military Data Mining
    Security Data Mining
    Social Science Mining
    Data Mining in Logistics
    Others

We particularly encourage submissions of industrial applications and case studies from practitioners. These will not be evaluated using solely theoretical research criteria, but will take general interest and presentation into consideration.
Data Mining Software

    All aspects and modules

Alternative and additional examples of possible topics include:

    Data Mining for Business Intelligence
    Emerging technologies in data mining
    Big Data
    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, ...)