Handling Dynamic Weights in Weighted Frequent Pattern Mining

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

doi:10.1093/ietisy/e91-d.11.2578

Handling Dynamic Weights in Weighted Frequent Pattern Mining

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

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Even though weighted frequent pattern (WFP) mining is more effective than traditional frequent pattern mining because it can consider different semantic significances (weights) of items, existing WFP algorithms assume that each item has a fixed weight. But in real world scenarios, the weight (price or significance) of an item can vary with time. Reflecting these changes in item weight is necessary in several mining applications, such as retail market data analysis and web click stream analysis. In this paper, we introduce the concept of a dynamic weight for each item, and propose an algorithm, DWFPM (dynamic weighted frequent pattern mining), that makes use of this concept. Our algorithm can address situations where the weight (price or significance) of an item varies dynamically. It exploits a pattern growth mining technique to avoid the level-wise candidate set generation-and-test methodology. Furthermore, it requires only one database scan, so it is eligible for use in stream data mining. An extensive performance analysis shows that our algorithm is efficient and scalable for WFP mining using dynamic weights.

Publication: IEICE TRANSACTIONS on Information Vol.E91-D No.11 pp.2578-2588

Publication Date: 2008/11/01

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Online ISSN: 1745-1361

DOI: 10.1093/ietisy/e91-d.11.2578

Type of Manuscript: Special Section PAPER (Special Section on Knowledge, Information and Creativity Support System)

Category: Knowledge Discovery and Data Mining

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Chowdhury Farhan AHMED, Syed Khairuzzaman TANBEER, Byeong-Soo JEONG, Young-Koo LEE, "Handling Dynamic Weights in Weighted Frequent Pattern Mining" in IEICE TRANSACTIONS on Information, vol. E91-D, no. 11, pp. 2578-2588, November 2008, doi: 10.1093/ietisy/e91-d.11.2578.
Abstract: Even though weighted frequent pattern (WFP) mining is more effective than traditional frequent pattern mining because it can consider different semantic significances (weights) of items, existing WFP algorithms assume that each item has a fixed weight. But in real world scenarios, the weight (price or significance) of an item can vary with time. Reflecting these changes in item weight is necessary in several mining applications, such as retail market data analysis and web click stream analysis. In this paper, we introduce the concept of a dynamic weight for each item, and propose an algorithm, DWFPM (dynamic weighted frequent pattern mining), that makes use of this concept. Our algorithm can address situations where the weight (price or significance) of an item varies dynamically. It exploits a pattern growth mining technique to avoid the level-wise candidate set generation-and-test methodology. Furthermore, it requires only one database scan, so it is eligible for use in stream data mining. An extensive performance analysis shows that our algorithm is efficient and scalable for WFP mining using dynamic weights.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.11.2578/_p

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@ARTICLE{e91-d_11_2578,
author={Chowdhury Farhan AHMED, Syed Khairuzzaman TANBEER, Byeong-Soo JEONG, Young-Koo LEE, },
journal={IEICE TRANSACTIONS on Information},
title={Handling Dynamic Weights in Weighted Frequent Pattern Mining},
year={2008},
volume={E91-D},
number={11},
pages={2578-2588},
abstract={Even though weighted frequent pattern (WFP) mining is more effective than traditional frequent pattern mining because it can consider different semantic significances (weights) of items, existing WFP algorithms assume that each item has a fixed weight. But in real world scenarios, the weight (price or significance) of an item can vary with time. Reflecting these changes in item weight is necessary in several mining applications, such as retail market data analysis and web click stream analysis. In this paper, we introduce the concept of a dynamic weight for each item, and propose an algorithm, DWFPM (dynamic weighted frequent pattern mining), that makes use of this concept. Our algorithm can address situations where the weight (price or significance) of an item varies dynamically. It exploits a pattern growth mining technique to avoid the level-wise candidate set generation-and-test methodology. Furthermore, it requires only one database scan, so it is eligible for use in stream data mining. An extensive performance analysis shows that our algorithm is efficient and scalable for WFP mining using dynamic weights.},
keywords={},
doi={10.1093/ietisy/e91-d.11.2578},
ISSN={1745-1361},
month={November},}

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TY - JOUR
TI - Handling Dynamic Weights in Weighted Frequent Pattern Mining
T2 - IEICE TRANSACTIONS on Information
SP - 2578
EP - 2588
AU - Chowdhury Farhan AHMED
AU - Syed Khairuzzaman TANBEER
AU - Byeong-Soo JEONG
AU - Young-Koo LEE
PY - 2008
DO - 10.1093/ietisy/e91-d.11.2578
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2008
AB - Even though weighted frequent pattern (WFP) mining is more effective than traditional frequent pattern mining because it can consider different semantic significances (weights) of items, existing WFP algorithms assume that each item has a fixed weight. But in real world scenarios, the weight (price or significance) of an item can vary with time. Reflecting these changes in item weight is necessary in several mining applications, such as retail market data analysis and web click stream analysis. In this paper, we introduce the concept of a dynamic weight for each item, and propose an algorithm, DWFPM (dynamic weighted frequent pattern mining), that makes use of this concept. Our algorithm can address situations where the weight (price or significance) of an item varies dynamically. It exploits a pattern growth mining technique to avoid the level-wise candidate set generation-and-test methodology. Furthermore, it requires only one database scan, so it is eligible for use in stream data mining. An extensive performance analysis shows that our algorithm is efficient and scalable for WFP mining using dynamic weights.
ER -