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Mining transactional tree databases under homeomorphism

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Abstract

A key task in mining tree-structured data is finding frequent embedded tree patterns, which has two settings: the transactional setting and the per-occurrence setting. In the transactional setting, which is the focus of this paper, the crucial step is to decide whether a tree pattern is subtree homeomorphic to a database tree. Our extensive study on the properties of real-world tree-structured datasets reveals that while many vertices in a database tree may have the same label, no two vertices on the same path are identically labeled. In this paper, we exploit this property and propose a novel and efficient method for deciding whether a tree pattern is subtree homeomorphic to a database tree. Our algorithm is based on a compact data structure called EMET, which stores all information required for subtree homeomorphism. We propose an efficient algorithm to generate EMETs of larger patterns using EMETs of the smaller ones. Based on the proposed subtree homeomorphism method, we introduce TTM, an effective algorithm for finding frequent tree patterns from rooted ordered trees. We evaluate the efficiency of TTM on several real-world and synthetic datasets and show that it outperforms well-known existing algorithms by an order of magnitude.

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Data availability

The CSLOGS datasets, along with the tree generator program used to produce synthetic datasets, are publicly available on the internet. The NASA and Prions datasets were provided through email communications (please see the Acknowledgments section).

Code availability

The release of the code is limited by licensing constraints.

Notes

  1. EMET is an abbreviation for EMbedding Encoder for Transactional tree mining.

  2. TTM is an abbreviation for Transactional Tree Miner.

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Acknowledgements

We are thankful to Prof. Mohammed Javeed Zaki for providing the TreeMinerD code, the CSLOGS datasets and the TreeGenerator program, to Dr Henry Tan for providing the MB3Miner-T code, to Professor Jun-Hong Cui for providing the NASA dataset and to Dr Fedja Hadzic for providing the Prions dataset. Parts of this work were performed, while the second author was at Xerox Research Centre Europe, later known as Naver Labs Europe.

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  1. Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

    Mostafa Haghir Chehreghani

  2. Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden

    Morteza Haghir Chehreghani

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Mostafa Haghir Chehreghani developed ideas, implemented algorithms, ran experiments, analyzed results, and wrote paper.

Morteza Haghir Chehreghani improved ideas and wrote paper.

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Correspondence to Mostafa Haghir Chehreghani.

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Haghir Chehreghani, M., Haghir Chehreghani, M. Mining transactional tree databases under homeomorphism. J Supercomput 81, 530 (2025). https://doi.org/10.1007/s11227-025-06997-2

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