AIM

The Indian Journal of Data Mining (IJDM) aims to serve as a platform for integrating academic research, industry standards, and innovative concepts in the diverse field of Data Mining. It strives to disseminate high-quality, peer-reviewed articles in these domains. The content presented in the IJDM is indispensable for leaders overseeing various projects, programs, and portfolios. Recognizing the theoretical and managerial implications of the research is vital. The IJDM is interested in exceptional submissions that offer theoretical advancements and practical applications, emphasizing the importance of academic rigour and real-world significance.

SCOPE

The Indian Journal of Data Mining (IJDM) explores an extensive array of subjects within the field of Data Mining, encompassing various topics, including but not limited to:

  • Data Science
  • Big Data Analytics
  • Machine Learning
  • Pattern Recognition
  • Predictive Modelling
  • Data Warehouse
  • Data Visualization
  • Scalable Computing
  • Cloud Computing
  • Knowledge Discovery
  • Information Retrieval
  • Social Data and Semantics
  • Data Classification
  • Data Clustering
  • Data Association
  • Data Regression
  • Data Cleaning
  • Feature Selection and Extraction
  • Data Mining Algorithms
  • Data Mining Methodology
  • Generalized Linear Models
  • Naive Bayes
  • Minimum Description Length
  • Non-Negative Matrix Factorization
  • Support Vector Machines
  • Artificial Intelligence
  • Industrial Challenges in Data Mining
  • Case Studies in Data Mining