International Journal of Data Mining

About the Journal
IJDM aims to provide a professional forum for formulating, discussing and disseminating these solutions, which relate to the design, development, deployment, management, measurement, and adjustment of data warehousing, data mining, data modelling, data management, and other data analysis techniques. IJDM provides a communication channel between practitioners and academics to discuss problems, challenges and opportunities in all aspects of data mining.
IJDM therefore aims to provide a professional forum for examining the processes and results associated with obtaining data, as well as munging, scrubbing, exploring, modelling, interpreting, communicating and visualising data. The goal is an integrated and interconnected process designed to form a common ground from which a knowledge-based system can be built, shared and supported by professionals from different disciplines. The process of knowledge creation can include multiple components, including data acquisition/collection, data accumulation, data maturation, data selection and refining, data storage and retrieval, data pre-processing, data analysis and validation, data maintenance and data presentation, data warehousing, data mining and/or modelling, and information extraction. Therefore, data chain management cannot be isolated, separated, broken, or ignored. It is an integrated and interconnected process.

Topics Covered include:
Artificial intelligence
Business analytics/intelligence, process modelling
Computer science, database management systems
Data management, mining, modelling, warehousing
Data analysis in Environmental science, environment (ecoinformatics)
Management science, operations research, mathematics/statistics
Business/economics, (computational) finance
Data analysis in Healthcare, medicine, pharmaceuticals
Data analysis in (Computational) chemistry, biology (bioinformatics)
Big data cloud, mining and management
Big data storage, processing, sharing and visualisation
Big data systems, tools, theory and applications
Business analytics, intelligence and mathematics
Informatics and information systems and technology
Machine learning, web-based decision making
Management science, social sciences and statistics
Mathematical optimisation and mathematics of decision sciences
Multiple source data processing and integration
Optimisation, performance measurement
Volume, velocity and variety of big data on cloud