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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: October 11, 2018

Best-first search-based approach for mining top-k closed frequent itemsets from uncertain databases.

Nguyen Le1, Huy Vo1, Thien Nguyen2

  • 1Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam.

Plos One
|June 17, 2026
PubMed
Summary

This study introduces TUFCI, a novel algorithm for efficient uncertain data mining. TUFCI significantly speeds up the discovery of top-k closed frequent itemsets from uncertain databases.

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Area of Science:

  • Data Mining
  • Database Systems
  • Machine Learning

Background:

  • Data mining from uncertain data sources is crucial.
  • Mining top-k closed frequent itemsets is computationally intensive due to probabilistic support evaluation and large search spaces.
  • Existing depth-first search (DFS) methods often discover patterns late, leading to inefficient pruning and closure verification.

Purpose of the Study:

  • To propose TUFCI, a best-first-search algorithm for mining top-k closed frequent itemsets from uncertain databases.
  • To improve the efficiency and reduce the computational cost of uncertain data mining.
  • To enable early discovery of strong patterns and safe termination.

Main Methods:

  • TUFCI employs a best-first-search strategy using a priority queue.
  • Candidates are explored in descending order of probabilistic support.
  • Support-ordered exploration enhances closure checking by prioritizing supersets likely to violate the closure property.

Main Results:

  • TUFCI significantly outperforms DFS-based algorithms in terms of runtime.
  • The number of closure checks is substantially reduced, particularly on dense datasets.
  • Early discovery of strong patterns leads to rapid threshold elevation and safe early termination.

Conclusions:

  • TUFCI offers a more efficient approach to mining top-k closed frequent itemsets from uncertain databases.
  • The best-first-search strategy effectively prunes the search space and reduces redundant computations.
  • This method provides a significant advancement for handling large-scale uncertain data.