Please use this identifier to cite or link to this item: http://repository.vnu.edu.vn/handle/VNU_123/958
Title: An Efficient Tree-based Frequent Temporal Inter-object Pattern Mining Approach in Time Series Databases
Authors: Nguyen, Thanh Vu
Vo, Thi Ngoc Chau
Keywords: Frequent Temporal Inter-Object Pattern;Temporal P attern Tree;Temporal Pattern Mining;Support Count;Time Series Mining;Time Series Rule Mining
Issue Date: 2015
Publisher: H. : ĐHQGHN
Citation: tr. 1-21
Series/Report no.: Vo l. 31, No. 1;
Abstract: In order to make the most of time series present in many various application domains such as finance, medicine, geology, meteorology, etc., mining time series is performed for useful information and hidden knowledge. Discovered knowledge is very significant to help users such as data analysts and managers get fascinating insights into important temporal relationships of objects/phenomena along time. Unfortunately, two main challenges exist with frequent pattern mining in time series databases. The first challenge is the combinatorial explosion of too many possible combinations for frequent patterns with their detailed descriptions, and the second one is to determine frequent patterns truly meaningful and relevant to the users. In this paper, we propose a tree-based frequent temporal inter-object pattern mining algorithm to cope with these two challenges in a level-wise bottom-up approach. In comparison with the existing works, our proposed algorithm is more effective and efficient for frequent temporal inter-object patterns which are more informative with explicit and exact temporal information automatically discovered from a time series database. As shown in the experiments on real financial time series, our work has reduced many invalid combinations for frequent patterns and also avoided many irrelevant frequent patterns returned to the users. .
URI: http://repository.vnu.edu.vn/handle/VNU_123/958
ISSN: 0866-8612
Appears in Collections:Computer Science and Communication Engineering

Files in This Item:
Thumbnail

  • File : document.pdf
  • Description : 
  • Size : 345.41 kB
  • Format : Adobe PDF


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