A Narrative Approach for Removal Embedded Prototype from Big Tree Data
DOI:
https://doi.org/10.53555/nncse.v6i9.790Keywords:
Data mining, Algorithm design and analysis, Informatics, Computational modeling, Databases, Upper bound, Data modelsAbstract
Many modern functions and systems represent and exchange data in tree-structured form and process and produce large tree datasets. Discovering informative patterns in large tree datasets is an important research area that has many practical applications. We propose a novel approach that exploits efficient homomorphic pattern matching algorithms to compute pattern support incrementally and avoids the costly enumeration of all patterns matching required by previous approaches. To reduce space consumption, matching information of already computed patterns is materialized as bitmaps. We further optimize our basic support computation method by designing an algorithm which incrementally generates the bitmaps of the embeddings of a new candidate pattern without first explicitly computing the embeddings of this pattern. Our extensive experimental results on real and synthetic large-tree datasets show that our approach displays orders of magnitude performance improvements over a state-of-the-art tree mining algorithm and a recent graph mining algorithm.
References
Asai T, Arimura H, Uno T, Nakano S-I (2003) Discovering frequent substructures in large unordered trees. In: Discovery, Science, pp 47–61
Bruno N, Koudas N, and Srivastava D (2002) Holistic twig joins: optimal XML pattern matching. In: SIGMOD, pp 310–321.
Chi Y, Xia Y, Yang Y, Muntz RR (2005) Mining closed and maximal frequent subtrees from databases of labeled rooted trees. IEEE Trans Knowl Data Eng 17(2):190–202
Chi Y, Yang Y, and Muntz RR (2004) Hybridtreeminer: an efficient algorithm for mining frequent rooted trees and free trees using canonical form. In: SSDBM, pp 11–20
Chi Y, Yang Y, Muntz RR (2005) Canonical forms for labeled trees and their applications in frequent subtree mining. Knowl Inf Syst 8(2):203–234.
Dries A, Nijssen S (2012) Mining patterns in networks using homomorphism. In: SDM, pp 260–271
Feng Z, Hsu W, and Lee M-L (2005) Efficient pattern discovery for semistructured data. In: ICTAI, pp 294–301
Goethals B, Hoekx E, and den Bussche JV (2005) Mining tree queries in a graph. In: KDD, pp 61–69
Kibriya AM, Ramon J (2013) Nearly exact mining of frequent trees in large networks. Data Min Knowl Discov 27(3):478–504
Kilpela¨inen P, Mannila H (1995) Ordered and unordered tree inclusion. SIAM J Comput 24(2):340–356
Miklau G, Suciu D (2004) Containment and equivalence for a fragment of xpath. J ACM 51(1):2–45
Nijssen S, Kok JN (2004) A quickstart in frequent structure mining can make a difference. In: KDD, pp 647–652
Tan H, Hadzic F, Dillon TS, Chang E, Feng L (2008) Tree model guided candidate generation for mining frequent subtrees from xml documents. TKDD 2(2):1–43
Tatikonda S, Parthasarathy S, Kurc¸ TM (2006) Trips and tides: new algorithms for tree mining. In: CIKM, pp 455–464
Termier A, Rousset M-C, Sebag M (2002) Treefinder: a first step towards xml data mining. In: ICDM, pp 450–457.
Downloads
Published
Issue
Section
License
Copyright (c) 2019 Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552)

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.