A Survey of Frequent Subgraph Mining Algorithms

  • Authors

    • Chandra Prakash Dixit
    • Nilay Khare
    2018-07-07
    https://doi.org/10.14419/ijet.v7i3.8.15218
  • Data Mining, Frequent Subgraph Mining, Graph Algorithms, Indexing, Knowledge Data Discovery, Pattern Mining,

  • Abstract

    Graphs are broadly used data structure. Graphs are very useful in representing/analyzing and processing real world data. Evolving graphs are graphs which are frequently changing in nature. There is either increase or decrease in their size i.e. change in number of edges or/and vertices.  Mining is the process done for knowledge discovery in graphs. Detecting specific patterns with their number of repetition more than a predefined threshold in graph is known as frequent subgraph mining or FSM. Real Timed data representing graphs are high volumetric or of very large in size, handling such graphs require processing them with special mechanisms and algorithms. Our review paper discovers present FSM techniques and tries to give their comparative study.

     

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  • How to Cite

    Prakash Dixit, C., & Khare, N. (2018). A Survey of Frequent Subgraph Mining Algorithms. International Journal of Engineering & Technology, 7(3.8), 58-62. https://doi.org/10.14419/ijet.v7i3.8.15218

    Received date: 2018-07-06

    Accepted date: 2018-07-06

    Published date: 2018-07-07