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The Research Leagues events aim to release a wide range of articles that provide insight and explore the latest advancements in engineering, medicine, social science, applied science, management etc.

Our conference practices methodological, conceptual, and epistemological diversity. But we prefer articles that effectively engage with current intellectual debates. We expect our participants to contribute innovative ideas, perspectives, and research methods.

Our events can potentially revolutionize the current paradigm and pave the way for growth and development. Those who intend to have their original findings and research published through our events should get an idea from the Author Guidelines, Rules for Presentation, and Instruction sections, before anything else.

Engineering

• Foundations of data mining
• Data mining and machine learning algorithms and methods in traditional areas (such as classification, regression, clustering, probabilistic modeling, and association analysis), and in new areas
• Mining text and semi-structured data, and mining temporal, spatial and multimedia data
• Mining data streams
• Mining spatio-temporal data
• Mining with data clouds and Big Data
• Link and graph mining
• Pattern recognition and trend analysis
• Collaborative filtering/personalization
• Data and knowledge representation for data mining
• Query languages and user interfaces for mining
• Complexity, efficiency, and scalability issues in data mining
• Data pre-processing, data reduction, feature selection and feature transformation
• Post-processing of data mining results
• Statistics and probability in large-scale data mining
• Soft computing (including neural networks, fuzzy logic, evolutionary computation, and rough sets) and uncertainty management for data mining
• Integration of data warehousing, OLAP and data mining
• Human-machine interaction and visual data mining
• High performance and parallel/distributed data mining
• Quality assessment and interestingness metrics of data mining results
• Visual Analytics
• Security, privacy and social impact of data mining
• Data mining applications in bioinformatics, electronic commerce, Web, intrusion detection, finance, marketing, healthcare, telecommunications and other fields