AIM

The mission of the journal is to bring together thought, academic research and corporate practice in all areas of Data Mining in a reader-friendly format. The aim of the journal is to publish high quality peer–reviewed articles in the area of Data Mining. The research published in the journal is practically relevant, so that the results are useful for managers in leadership roles related to projects, programs, and portfolios. Therefore, both theoretical and managerial implications of the research need to be considered. In other words, the journal seeks great contributions to both theory and practice, and strongly values both rigor 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-blind peer review process Articles for the regular issue can be submitted electronically round the year. The scope includes (but 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 Regression
  • 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