Search Results for "frequent subgraph mining"

6 programs for "frequent subgraph mining" with 1 filter applied:

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  • 1
    This project aims to develop and share fast frequent subgraph mining and graph learning algorithms. Currently we release the frequent subgraph mining package FFSM and later we will include new functions for graph regression and classification package
    Downloads: 1 This Week
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  • 2

    Approximate Subgraph Matching Algorithm

    Approximate Subgraph Matching Algorithm for Dependency Graphs

    ..., the total worst-case algorithm complexity is O(m^n * n(n-1)/2 * km * log m). This Java implementation implements our ASM algorithm. See README file: https://sourceforge.net/projects/asmalgorithm/files/ If you use our ASM implementation to support academic research, please cite the following paper: Haibin Liu, Lawrence Hunter, Vlado Keselj, and Karin Verspoor. Approximate Subgraph Matching-based Literature Mining for Biomedical Events and Relations. PLOS ONE, 8:4 e60954, 2013.
    Downloads: 0 This Week
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  • 3
    Data mining tool for the extraction of spatio-temporal frequent patterns ("Trajectory patterns" or "T-patterns") from GPS-like trajectories of a set of moving objects. Work performed within the European project GeoPKDD - www.geopkdd.eu
    Downloads: 0 This Week
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  • 4
    baobab is an implementation of FPTrees or Frequent Pattern Trees, a pattern recognition/data mining technique. it has innumerable applications in language processing, clickstream analysis, etc.
    Downloads: 1 This Week
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  • 5
    DMTL (Data Mining Template Library) - A generic C++ based library for mining structured patterns such as sets, sequences, trees and graphs. The library provides implementation of popular frequent pattern mining algorithms.
    Downloads: 0 This Week
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  • 6
    This project implements the algorithm proposed in page 109-118, ACM SIGKDD, 2003, "Inverted matrix: efficient discovery of frequent items in large datasets in the context of interactive mining" and its improvements.
    Downloads: 0 This Week
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