Mining Rare Patterns Using Hyper-Linked Data Structure View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017-11-01

AUTHORS

Anindita Borah , Bhabesh Nath

ABSTRACT

Rare pattern mining has emerged as a compelling field of research over the years. Experimental results from literature illustrate that tree-based approaches are most efficient among the rare pattern mining techniques. Despite their significance and implication, tree-based approaches become inefficient while dealing with sparse data and data with short patterns and also suffer from the limitation of memory. In this study, an efficient rare pattern mining technique has been proposed that employs a hyper-linked data structure to overcome the shortcomings of tree data structure based approaches. The hyper-linked data structure enables dynamic adjustment of links during the mining process that reduces the space overhead and performs better with sparse datasets. More... »

PAGES

467-472

Book

TITLE

Pattern Recognition and Machine Intelligence

ISBN

978-3-319-69899-1
978-3-319-69900-4

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-69900-4_59

DOI

http://dx.doi.org/10.1007/978-3-319-69900-4_59

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1092444311


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