Journal Information
Data Mining and Knowledge Discovery
http://www.springer.com/computer/database+management+%26+information+retrieval/journal/10618
Impact Factor:
3.16
Publisher:
Springer
ISSN:
1384-5810
Viewed:
5779
Tracked:
20

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Call For Papers
Advances in data gathering, storage, and distribution have created a need for computational tools and techniques to aid in data analysis. Data Mining and Knowledge Discovery in Databases (KDD) is a rapidly growing area of research and application that builds on techniques and theories from many fields, including statistics, databases, pattern recognition and learning, data visualization, uncertainty modelling, data warehousing and OLAP, optimization, and high performance computing.

KDD is concerned with issues of scalability, the multi-step knowledge discovery process for extracting useful patterns and models from raw data stores (including data cleaning and noise modelling), and issues of making discovered patterns understandable.

Data Mining and Knowledge Discovery is the premier technical publication in the field, providing a resource collecting relevant common methods and techniques and a forum for unifying the diverse constituent research communities. The journal publishes original technical papers in both the research and practice of DMKD, surveys and tutorials of important areas and techniques, and detailed descriptions of significant applications. Short (2-4 pages) application summaries are published in a special section.

The journal accepts paper submissions of any work relevant to DMKD. A summary of the scope of Data Mining and Knowledge Discovery includes:

Theory and Foundational Issues: Data and knowledge representation; modelling of structured, textual, and multimedia data; uncertainty management; metrics of interestingness and utility of discovered knowledge; algorithmic complexity, efficiency, and scalability issues in data mining; statistics over massive data sets.

Data Mining Methods: including classification, clustering, probabilistic modelling, prediction and estimation, dependency analysis, search, and optimization.

Algorithms for data mining including spatial, textual, and multimedia data (e.g., the Web), scalability to large databases, parallel and distributed data mining techniques, and automated discovery agents.

Knowledge Discovery Process: Data pre-processing for data mining, including data cleaning, selection, efficient sampling, and data reduction methods; evaluating, consolidating, and explaining discovered knowledge; data and knowledge visualization; interactive data exploration and discovery.
Last updated by Dou Sun in 2017-09-15
Special Issues
Special Issue on Data Mining for Geosciences
Submission Date: 2017-11-01

Modern geosciences have to deal with large quantities and a wide variety of data, including 2-D, 3-D and 4-D seismic surveys, well logs generated by sensors, detailed lithological records, satellite images and meteorological records. These data serve important industries, such as the exploration of mineral deposits and the production of energy (Oil and Gas, Geothermal, Wind, Hydroelectric), are important in the study of the earth crust to reduce the impact of earthquakes, in land use planning, and have a fundamental role in sustainability. In particular, the process of exploring and exploiting Oil and Gas (O&G) generates a lot of data that can bring more efficiency to the industry. The opportunities for using data mining techniques in the "digital oil-field" remain largely unexplored or uncharted. The purpose of this special issue is to be a breaking-edge showcase for applications and developments of data mining and knowledge discovery in the area of the geosciences with a special focus in the oil and gas exploration. Researchers are invited to submit original papers presenting novel data mining methodologies or applications to the geosciences, including but not limited to the following topics: - Oil and gas exploration and production - Mineral deposit/reservoir identification and characterization - Exploration of well-log data - Earth crust analysis and understanding - Sensor data exploration - Remote sensing - Novel data mining problems in the geosciences - Visualization of big data in the geosciences - Geoscience data fusion for enhancing data mining solutions - Data streams analysis in geoscience - Feature extraction and data transformation from geoscientific data
Last updated by Dou Sun in 2017-09-15
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