AIM
The journal’s mission is to bring together thought, academic research and corporate practice in all areas of Data Mining in a reader-friendly format. The journal aims to publish high-quality peer–reviewed articles in the area of Data Mining. The research published in the journal is practically relevant, so the results are helpful for managers in leadership roles related to projects, programs, and portfolios. Therefore, the research’s theoretical and managerial implications need to be considered. In other words, the journal seeks excellent contributions to theory and practice and strongly values rigour and relevance.
SCOPE
The Editors reserve the right to reject article(s) without sending them out for review. Submitted articles must be within the scope of the journal. All submitted articles go through a double-anonymized peer review process. Articles for the regular issue can be submitted electronically throughout the year. The area includes (but is not limited to) the following topics related to Data Mining:
- Data Mining
- Data Science
- Big Data
- Data Warehouse
- Visualization
- Security
- Privacy
- Big DaaS
- Scalable Computing
- Cloud Computing
- Knowledge Discovery
- Integration
- Transformation
- Information Retrieval
- Social Data and Semantics
- Mining Functions
- Data Classification
- Anomaly Detection
- Data Clustering
- Data Association
- Data Regression
- Data Cleaning
- Feature Selection and Extraction
- Data Mining Algorithms
- Apriori
- Decision Tree
- Generalized Linear Models
- k-Means
- Minimum Description Length
- Naive Bayes
- Non-Negative Matrix Factorization
- 0-Cluster
- Support Vector Machines
- Data Preparation
- Mining Unstructured Data
- Artificial Intelligence
- Future Directions and Challenges in Data Mining
- Industrial Challenges in Data Mining