PatternIQ Mining (PIQM) Sahara Digital Publications · eISSN 3006-8894

Aims and Scope

Aims

PatternIQ Mining (PIQM) is an international, open access, double-blind peer-reviewed journal published quarterly in English by Sahara Digital Publications, Sharjah, United Arab Emirates. The journal publishes original research on the discovery, analysis and use of patterns in data, and on the application of pattern intelligence across disciplines.

PIQM aims to:

  • publish rigorous, reproducible research that advances the theory and practice of pattern mining and pattern recognition;
  • give researchers and practitioners a venue in which new methods, systems and applications can be shared without cost to authors or readers;
  • encourage work that connects pattern analysis with real problems in science, industry and society.

Scope

The journal welcomes submissions in areas including, but not limited to:

  • Frequent, sequential, graph, spatial and temporal pattern mining; association rules and high-utility pattern mining
  • Pattern recognition and classification for image, video, signal, speech and text data
  • Machine learning and deep learning methods for pattern discovery and representation
  • Anomaly, outlier and fraud detection
  • Data mining, big data analytics and data stream mining
  • Computational intelligence, metaheuristics and swarm optimisation applied to pattern analysis
  • Explainable, privacy-preserving and federated approaches to pattern learning
  • Intelligent systems and applications of pattern analysis in healthcare, education, agriculture, finance, cybersecurity, transportation, energy, smart cities and industry

Manuscripts that fall outside this scope are returned to the authors without external review.

Article types

  • Original research articles
  • Review articles and systematic reviews
  • Short communications
  • Technical and methodological papers
  • Case studies and application-oriented research articles

Readership

Academic researchers, graduate students and industry practitioners working in data mining, machine learning, artificial intelligence and their fields of application.

Publication details