17
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Fast Generation of Best Interval Patterns for Nonmonotonic Constraints

      Preprint
      , ,

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          In pattern mining, the main challenge is the exponential explosion of the set of patterns. Typically, to solve this problem, a constraint for pattern selection is introduced. One of the first constraints proposed in pattern mining is support (frequency) of a pattern in a dataset. Frequency is an anti-monotonic function, i.e., given an infrequent pattern, all its superpatterns are not frequent. However, many other constraints for pattern selection are not (anti-)monotonic, which makes it difficult to generate patterns satisfying these constraints. In this paper we introduce the notion of projection-antimonotonicity and \(\theta\)-\(\Sigma\o\phi\iota\alpha\) algorithm that allows efficient generation of the best patterns for some nonmonotonic constraints. In this paper we consider stability and \(\Delta\)-measure, which are nonmonotonic constraints, and apply them to interval tuple datasets. In the experiments, we compute best interval tuple patterns w.r.t. these measures and show the advantage of our approach over postfiltering approaches. KEYWORDS: Pattern mining, nonmonotonic constraints, interval tuple data

          Related collections

          Most cited references10

          • Record: found
          • Abstract: not found
          • Article: not found

          Efficient mining of association rules using closed itemset lattices

            Bookmark
            • Record: found
            • Abstract: not found
            • Conference Proceedings: not found

            CloSpan: Mining: Closed Sequential Patterns in Large Datasets

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Mining itemset utilities from transaction databases

                Bookmark

                Author and article information

                Journal
                2015-06-02
                2015-06-16
                Article
                1506.01071
                a114f4f5-ddde-4a8a-a3f4-c9ef0213ef9f

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                18 pages; 2 figures; 2 tables; 1 algorithm; PKDD 2015 Conference Scientific Track
                cs.AI cs.DS

                Data structures & Algorithms,Artificial intelligence
                Data structures & Algorithms, Artificial intelligence

                Comments

                Comment on this article