Corpus ID: 15543248

Significant Pattern Mining on Continuous Variables

  title={Significant Pattern Mining on Continuous Variables},
  author={M. Sugiyama and K. Borgwardt},
  • M. Sugiyama, K. Borgwardt
  • Published 2017
  • Mathematics, Computer Science
  • ArXiv
  • Significant pattern mining, the search for sets of binary features that are statistically significantly enriched in a class of objects, is of fundamental importance in a wide range of applications from economics to statistical genetics. Still, all existing approaches make the restrictive assumption that the features are binary and require a binarization of continuous data during preprocessing, which often leads to a loss of information. Here, we solve the open problem of significant pattern… CONTINUE READING
    2 Citations

    Figures, Tables, and Topics from this paper

    A Modified C4.5 Classification Algorithm: With the Discretization Method in Calculating the Goodness Score Equivalent


    Significant Pattern Mining with Confounding Variables
    • 10
    • Highly Influential
    Discovering Significant Patterns
    • 211
    • Highly Influential
    • PDF
    Itemsets for Real-Valued Datasets
    • Nikolaj Tatti
    • Computer Science, Mathematics
    • 2013 IEEE 13th International Conference on Data Mining
    • 2013
    • 7
    • PDF
    Finding significant combinations of features in the presence of categorical covariates
    • 23
    • PDF
    Mining gene expression data with pattern structures in formal concept analysis
    • 196
    • Highly Influential
    • PDF
    Significant Subgraph Mining with Multiple Testing Correction
    • 25
    • PDF
    Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing
    • 38
    • PDF
    Revisiting Numerical Pattern Mining with Formal Concept Analysis
    • 87
    • Highly Influential
    • PDF
    Kingfisher: an efficient algorithm for searching for both positive and negative dependency rules with statistical significance measures
    • W. Hämäläinen
    • Mathematics, Computer Science
    • Knowledge and Information Systems
    • 2011
    • 35
    • Highly Influential
    • PDF
    Contrast Data Mining: Concepts, Algorithms, and Applications
    • 81