An Efficient Algorithm for Sliding Window-Based Weighted Frequent Pattern Mining over Data Streams

Chowdhury Farhan AHMED; Syed Khairuzzaman TANBEER; Byeong-Soo JEONG; Young-Koo LEE

doi:10.1587/transinf.E92.D.1369

An Efficient Algorithm for Sliding Window-Based Weighted Frequent Pattern Mining over Data Streams

Chowdhury Farhan AHMED, Syed Khairuzzaman TANBEER, Byeong-Soo JEONG, Young-Koo LEE

Full Text Views

0

Cite this

Summary :

Traditional frequent pattern mining algorithms do not consider different semantic significances (weights) of the items. By considering different weights of the items, weighted frequent pattern (WFP) mining becomes an important research issue in data mining and knowledge discovery area. However, the existing state-of-the-art WFP mining algorithms consider all the data from the very beginning of a database to discover the resultant weighted frequent patterns. Therefore, their approaches may not be suitable for the large-scale data environment such as data streams where the volume of data is huge and unbounded. Moreover, they cannot extract the recent change of knowledge in a data stream adaptively by considering the old information which may not be interesting in the current time period. Another major limitation of the existing algorithms is to scan a database multiple times for finding the resultant weighted frequent patterns. In this paper, we propose a novel large-scale algorithm WFPMDS (Weighted Frequent Pattern Mining over Data Streams) for sliding window-based WFP mining over data streams. By using a single scan of data stream, the WFPMDS algorithm can discover important knowledge from the recent data elements. Extensive performance analyses show that our proposed algorithm is very efficient for sliding window-based WFP mining over data streams.

Publication: IEICE TRANSACTIONS on Information Vol.E92-D No.7 pp.1369-1381

Publication Date: 2009/07/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.E92.D.1369

Type of Manuscript: Special Section PAPER (Special Section on Large Scale Algorithms for Learning and Optimization)

Category

Cite this

Copy

Chowdhury Farhan AHMED, Syed Khairuzzaman TANBEER, Byeong-Soo JEONG, Young-Koo LEE, "An Efficient Algorithm for Sliding Window-Based Weighted Frequent Pattern Mining over Data Streams" in IEICE TRANSACTIONS on Information, vol. E92-D, no. 7, pp. 1369-1381, July 2009, doi: 10.1587/transinf.E92.D.1369.
Abstract: Traditional frequent pattern mining algorithms do not consider different semantic significances (weights) of the items. By considering different weights of the items, weighted frequent pattern (WFP) mining becomes an important research issue in data mining and knowledge discovery area. However, the existing state-of-the-art WFP mining algorithms consider all the data from the very beginning of a database to discover the resultant weighted frequent patterns. Therefore, their approaches may not be suitable for the large-scale data environment such as data streams where the volume of data is huge and unbounded. Moreover, they cannot extract the recent change of knowledge in a data stream adaptively by considering the old information which may not be interesting in the current time period. Another major limitation of the existing algorithms is to scan a database multiple times for finding the resultant weighted frequent patterns. In this paper, we propose a novel large-scale algorithm WFPMDS (Weighted Frequent Pattern Mining over Data Streams) for sliding window-based WFP mining over data streams. By using a single scan of data stream, the WFPMDS algorithm can discover important knowledge from the recent data elements. Extensive performance analyses show that our proposed algorithm is very efficient for sliding window-based WFP mining over data streams.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.1369/_p

Copy

@ARTICLE{e92-d_7_1369,
author={Chowdhury Farhan AHMED, Syed Khairuzzaman TANBEER, Byeong-Soo JEONG, Young-Koo LEE, },
journal={IEICE TRANSACTIONS on Information},
title={An Efficient Algorithm for Sliding Window-Based Weighted Frequent Pattern Mining over Data Streams},
year={2009},
volume={E92-D},
number={7},
pages={1369-1381},
abstract={Traditional frequent pattern mining algorithms do not consider different semantic significances (weights) of the items. By considering different weights of the items, weighted frequent pattern (WFP) mining becomes an important research issue in data mining and knowledge discovery area. However, the existing state-of-the-art WFP mining algorithms consider all the data from the very beginning of a database to discover the resultant weighted frequent patterns. Therefore, their approaches may not be suitable for the large-scale data environment such as data streams where the volume of data is huge and unbounded. Moreover, they cannot extract the recent change of knowledge in a data stream adaptively by considering the old information which may not be interesting in the current time period. Another major limitation of the existing algorithms is to scan a database multiple times for finding the resultant weighted frequent patterns. In this paper, we propose a novel large-scale algorithm WFPMDS (Weighted Frequent Pattern Mining over Data Streams) for sliding window-based WFP mining over data streams. By using a single scan of data stream, the WFPMDS algorithm can discover important knowledge from the recent data elements. Extensive performance analyses show that our proposed algorithm is very efficient for sliding window-based WFP mining over data streams.},
keywords={},
doi={10.1587/transinf.E92.D.1369},
ISSN={1745-1361},
month={July},}

Copy

TY - JOUR
TI - An Efficient Algorithm for Sliding Window-Based Weighted Frequent Pattern Mining over Data Streams
T2 - IEICE TRANSACTIONS on Information
SP - 1369
EP - 1381
AU - Chowdhury Farhan AHMED
AU - Syed Khairuzzaman TANBEER
AU - Byeong-Soo JEONG
AU - Young-Koo LEE
PY - 2009
DO - 10.1587/transinf.E92.D.1369
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2009
AB - Traditional frequent pattern mining algorithms do not consider different semantic significances (weights) of the items. By considering different weights of the items, weighted frequent pattern (WFP) mining becomes an important research issue in data mining and knowledge discovery area. However, the existing state-of-the-art WFP mining algorithms consider all the data from the very beginning of a database to discover the resultant weighted frequent patterns. Therefore, their approaches may not be suitable for the large-scale data environment such as data streams where the volume of data is huge and unbounded. Moreover, they cannot extract the recent change of knowledge in a data stream adaptively by considering the old information which may not be interesting in the current time period. Another major limitation of the existing algorithms is to scan a database multiple times for finding the resultant weighted frequent patterns. In this paper, we propose a novel large-scale algorithm WFPMDS (Weighted Frequent Pattern Mining over Data Streams) for sliding window-based WFP mining over data streams. By using a single scan of data stream, the WFPMDS algorithm can discover important knowledge from the recent data elements. Extensive performance analyses show that our proposed algorithm is very efficient for sliding window-based WFP mining over data streams.
ER -