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    Area of Science:

    • Computer Science
    • Data Mining
    • Graph Theory

    Background:

    • Discovering frequent subtree patterns is crucial in various domains, including bioinformatics and web mining.
    • Existing tree mining algorithms often struggle with efficiently handling restrictedly embedded patterns in unordered trees.

    Purpose of the Study:

    • To develop an efficient algorithm for discovering restrictedly embedded subtree patterns in rooted labeled unordered trees.
    • To introduce novel Apriori-based techniques for candidate subtree generation and pattern detection.

    Main Methods:

    • Studied properties of a canonical form for unordered trees.
    • Developed Apriori-based techniques with pairwise joining and leg attachment for candidate subtree generation.
    • Utilized restricted edit distance for detecting restrictedly embedded subtrees.
    • Integrated these methods into the Frequent Restrictedly Embedded Subtree Miner (FRESTM) algorithm.

    Main Results:

    • The FRESTM algorithm correctly identifies frequent restrictedly embedded subtree patterns.
    • The algorithm's time and space complexities were analyzed.
    • Experimental results validated the effectiveness of FRESTM on synthetic and real-world datasets.

    Conclusions:

    • The proposed FRESTM algorithm offers an effective solution for the challenging problem of mining restrictedly embedded subtree patterns.
    • The novel techniques enhance the efficiency and accuracy of tree mining processes.