Abstract

Sequential pattern mining is one of popular data mining technique with sequential pattern as representation of data. However, most of sequential pattern mining research was conducted for structured data. In this paper, we did literature review of the sequential pattern mining algorithm that suitable for unstructured data such as text data. We reviewed several sequential pattern mining algorithm that had already used in text mining research, among others GSP, Spade, PrefixSpan, Spam, Lapin, SM-Spam, CM-Spade, BIDE, and another various algorithm based on sequential pattern mining problem such as concise representation and how to extract more rich pattern. The result showed that that from year to year research on text data using sequential pattern mining had increased. Although, not many algorithm were developed and also still rarely new algorithms were implemented in text data.

Details

Title
The concept of sequential pattern mining for text
Author
Maylawati, D S 1 ; Aulawi, H 2 ; Ramdhani, M A 3 

 Departement of Informatics, Sekolah Tinggi Teknologi Garut, Jalan Mayor Syamsu No 1 Tarogong Kidul Kabupaten Garut 44151, Indonesia 
 Industrial Engineering, Sekolah Tinggi Teknologi Garut, Jalan Mayor Syamsu No 1 Tarogong Kidul Kabupaten Garut 44151, Indonesia 
 Departement of Informatics, UIN Sunan Gunung Djati Bandung, Jalan A H Nasution No 105 Bandung 40614, Indonesia 
Publication year
2018
Publication date
Nov 2018
Publisher
IOP Publishing
ISSN
17578981
e-ISSN
1757899X
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2557157026
Copyright
© 2018. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.