DMIN 2015 (International Conference on Data Mining) is an academic conference held in Las Vegas, Nevada, USA on 2015-07-27. The paper submission deadline is 2015-04-15 (extended). Acceptance notifications are sent on 2015-04-30.
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, ...)
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