An improved frequent pattern tree: the child structured frequent pattern tree CSFP-tree

Title

An improved frequent pattern tree: the child structured frequent pattern tree CSFP-tree

Creator

Jamsheela O.; Raju G.

Description

Frequent itemsets are itemsets that occur frequently in a dataset. Frequent itemset mining extracts specific itemsets with supports higher than or equal to a minimum support threshold. Many mining methods have been proposed but Apriori and FP-growth are still regarded as two prominent algorithms. The performance of the frequent itemset mining depends on many factors; one of them is searching the nodes while constructing the tree. This paper introduces a new prefix-tree structure called child structured frequent pattern tree (CSFP-tree), an FP-tree attached with a child search subtree to each node. The experimental results reveal that the CSFP-tree is superior to the FP-tree and its new variations for any kind of datasets. 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

Source

Pattern Analysis and Applications, Vol-26, No. 2, pp. 437-454.

Date

2023-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

CSFP-tree; CSFP-tree mining; Data mining; FP-tree; Frequent itemset mining; Improved FP-tree

Coverage

Jamsheela O., EMEA College of Arts and Science, Kerala, Kondotty, India; Raju G., Christ University Yeshwantpur Campus, Bengaluru, India

Rights

Restricted Access

Relation

ISSN: 14337541

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1007/s10044-022-01111-1

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138767834&doi=10.1007%2fs10044-022-01111-1&partnerID=40&md5=ab7fafe473978832928f776417b5843f