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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, ...)</pre><div class="alert alert-warning"> Last updated by <a href="/researcher/1">Dou Sun</a> in 2015-04-03</div> </div> </div>  <div class="portlet" id="yw2"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-th"></i> Related Conferences</div> </div> <div class="portlet-content"> <div class="hidden-phone"> <div id="yw3" class="grid-view"> <table class="table table-hover table-striped table-bordered table-condensed"> <thead> <tr> <th id="yw3_c0">CCF</th><th id="yw3_c1">CORE</th><th id="yw3_c2">QUALIS</th><th 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