The Semantic Adjacency Criterion in Time Intervals Mining

Alexander Shknevsky, Yuval Shahar, Robert Moskovitch

Research output: Contribution to journalArticlepeer-review


We propose a new pruning constraint when mining frequent temporal patterns to be used as classification and prediction features, the Semantic Adjacency Criterion [SAC], which filters out temporal patterns that contain potentially semantically contradictory components, exploiting each medical domain’s knowledge. We have defined three SAC versions and tested them within three medical domains (oncology, hepatitis, diabetes) and a frequent-temporal-pattern discovery framework. Previously, we had shown that using SAC enhances the repeatability of discovering the same temporal patterns in similar proportions in different patient groups within the same clinical domain. Here, we focused on SAC’s computational implications for pattern discovery, and for classification and prediction, using the discovered patterns as features, by four different machine-learning methods: Random Forests, Naïve Bayes, SVM, and Logistic Regression. Using SAC resulted in a significant reduction, across all medical domains and classification methods, of up to 97% in the number of discovered temporal patterns, and in the runtime of the discovery process, of up to 98%. Nevertheless, the highly reduced set of only semantically transparent patterns, when used as features, resulted in classification and prediction models whose performance was at least as good as the models resulting from using the complete temporal-pattern set.

Original languageAmerican English
Article number173
JournalBig Data and Cognitive Computing
Issue number4
StatePublished - 1 Dec 2023


  • classification
  • frequent temporal pattern mining
  • machine learning
  • medicine
  • prediction
  • semantics
  • temporal data mining
  • time intervals mining

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Information Systems
  • Computer Science Applications
  • Artificial Intelligence


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