X-FSPMiner: A Novel Algorithm for Frequent Similar Pattern Mining-Reference-Cited by-同舟云学术

X-FSPMiner: A Novel Algorithm for Frequent Similar Pattern Mining

Published:2024-01-30 Issue: Volume: Page:
ISSN:1556-4681
Container-title:ACM Transactions on Knowledge Discovery from Data
language:en
Short-container-title:ACM Trans. Knowl. Discov. Data

Author:

Rodríguez-González Ansel Y.¹,Aranda Ramón²,Álvarez-Carmona Miguel Á.³,Díaz-Pacheco Angel⁴,Rosas Rosa María Valdovinos⁵

Affiliation:

1. Unidad de Transferencia Tecnológica, Centro de Investigación Científica y de Educación Superior de Ensenada, Tepic, México

2. Centro de Investigación en Matemáticas, A.C., Unidad Mérida, Mérida, México

3. Centro de Investigación en Matemáticas, A.C., Unidad Monterrey, Apodaca, México

4. Universidad de Guanajuato, División de Ingenierías, Campus Irapuato-Salamanca, Salamanca, México

5. Facultad de Ingeniería, Universidad Autónoma del Estado de México, Toluca de Lerdo, México

Abstract

Frequent similar pattern mining (FSP mining) allows found frequent patterns hidden from the classical approach. However, the use of similarity functions implies more computational effort, becoming necessary to develop more efficient algorithms for FSP mining. This work aims to improve the efficiency of mining all FSPs when using Boolean and non-increasing monotonic similarity functions. A data structure to condense an object description collection named FV-Tree , and an algorithm for mine all FSP from the FV-Tree , named X-FSPMiner , are proposed. The experimental results reveal that the novel algorithm X-FSPMiner vastly outperforms the state-of-the-art algorithms for mine all FSP using Boolean and non-increasing monotonic similarity functions.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Link

https://dl.acm.org/doi/pdf/10.1145/3643820

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