Please use this identifier to cite or link to this item: https://dspace.ctu.edu.vn/jspui/handle/123456789/56276
Title: Mining top-K frequent sequential pattern in item interval extended sequence database
Authors: Tran, Huy Duong
Nguyen, Truong Thang
Vu, Duc Thi
Keywords: Sequential pattern
Item interval
Top-K
Issue Date: 2018
Series/Report no.: Journal of Computer Science and Cybernetics;Vol. 34, No. 03 .- P.249–264
Abstract: Frequent sequential pattern mining in item interval extended sequence database (iSDB) has been one of the interesting tasks in recent years. Unlike classic frequent sequential pattern mining, the pattern mining in iSDB also considers the item interval between successive items; thus, it may extract more meaningful sequential patterns in real life. Most previous frequent sequential pattern mining in iSBD algorithms needs a minimum support threshold (minsup) to perform the mining. However, it’s not easy for users to provide an appropriate threshold in practice. The too high minsup value will lead to missing valuable patterns, while the too low minsup value may generate too many useless patterns. To address this problem, we propose an algorithm: TopKWFP – top – K weighted frequent sequential pattern mining in item interval extended sequence database. Our algorithm doesn’t need to provide a fixed minsup value, this minsup value will dynamically raise during the mining process.
URI: https://dspace.ctu.edu.vn/jspui/handle/123456789/56276
ISSN: 1813-9663
Appears in Collections:Tin học và Điều khiển học (Journal of Computer Science and Cybernetics)

Files in This Item:
File Description SizeFormat 
_file_
  Restricted Access
5.78 MBAdobe PDF
Your IP: 18.226.34.148


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.