Classification and Sequential Pattern Mining From Uncertain Datasets

• Author / Creator

  Hooshsadat, Metanat

• Several research projects explore the application of uncertain databases which contain
  probabilistic attributes. Uncertainty in data can be caused by inherent randomness,
  imprecision in measuring equipment, ambiguity, information extraction from
  unstructured data, etc.
  The classification and Sequential Pattern Mining (SPM) of uncertain datasets
  both play a vital role in decision making systems and have recently attracted significant
  attention. In this study, we propose two novel algorithms for the aforementioned
  problems. Our novel associative classifier for uncertain data, UAC, has an
  effective rule pruning strategy. Using a general model for uncertainty, our experiments
  show that in many cases, UAC reaches higher accuracies than the existing
  algorithms.
  In SPM for uncertain data, other studies aimed to solve the problem for specific
  uncertainty models. We introduce UAprioriAll which conducts SPM from datasets
  with general attribute level uncertainty. Our experiments show that this method
  scales linearly when increasing the number of transactions.

• Subjects / Keywords

  • Uncertain Datasets
  • Associative Classification
  • Expected Support
  • Sequential Patterns

• Graduation date

  Fall 2011

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R39T2P

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.