Approximate high utility itemset mining in noisy environments

High utility pattern mining has been proposed to overcome the limitations of frequent pattern mining which cannot reflect the unique profits of items. High utility pattern mining has been actively conducted because it can find more valuable patterns than previous fields of pattern mining. However, i...

Mô tả đầy đủ

Đã lưu trong:

Chi tiết về thư mục
Những tác giả chính:	Yoonji Baek, Unil Yun, Heonho Kim, Jongseong Kim, Bay Vo, Trương, Chí Tín, Zhi-Hong Deng
Định dạng:	Journal article
Ngôn ngữ:	English
Được phát hành:	Elsevier B.V. 2021
Những chủ đề:	Approximation Error tolerance Approximate mining Utility itemset mining
Truy cập trực tuyến:	http://scholar.dlu.edu.vn/handle/123456789/591
Các nhãn:	Thêm thẻ Không có thẻ, Là người đầu tiên thẻ bản ghi này!

Thư viện lưu trữ:	Thư viện Trường Đại học Đà Lạt

id	oai:scholar.dlu.edu.vn:123456789-591
record_format	dspace
spelling	oai:scholar.dlu.edu.vn:123456789-5912022-08-03T23:45:57Z Approximate high utility itemset mining in noisy environments Yoonji Baek Unil Yun Heonho Kim Jongseong Kim Bay Vo Trương, Chí Tín Zhi-Hong Deng Approximation Error tolerance Approximate mining Utility itemset mining High utility pattern mining has been proposed to overcome the limitations of frequent pattern mining which cannot reflect the unique profits of items. High utility pattern mining has been actively conducted because it can find more valuable patterns than previous fields of pattern mining. However, its traditional approaches are designed to perform on the assumption that the data stored in databases is faultless. If there are unknown errors, such as noises, in a given database, the mining results traditional high utility pattern mining approaches mined in this database cannot be fully trusted. In this paper, a novel technique considering the noises is suggested in order to overcome this limitation. The proposed technique calculates the ranges of trustworthy utilities for patterns using a utility tolerance factor. By using this factor, the robust high utility patterns, called as approximate high utility patterns, can be extracted from a noisy database. To evaluate the performance of the proposed algorithm, various experiments are designed and conducted in terms of runtime, memory usage, and scalability. The experimental results show that the proposed algorithm outperforms than competitors, an apriori-based approach and UP-Growth. 212 2021-09-23T09:35:06Z 2021-09-23T09:35:27Z 2021-09-23T09:35:06Z 2021-09-23T09:35:27Z 2021-01 Journal article Bài báo đăng trên tạp chí thuộc ISI, bao gồm book chapter http://scholar.dlu.edu.vn/handle/123456789/591 10.1016/j.knosys.2020.106596 en Knowledge-Based Systems 10.1016/j.knosys.2020.106596 0950-7051 Elsevier B.V.
institution	Thư viện Trường Đại học Đà Lạt
collection	Thư viện số
language	English
topic	Approximation Error tolerance Approximate mining Utility itemset mining
spellingShingle	Approximation Error tolerance Approximate mining Utility itemset mining Yoonji Baek Unil Yun Heonho Kim Jongseong Kim Bay Vo Trương, Chí Tín Zhi-Hong Deng Approximate high utility itemset mining in noisy environments
description	High utility pattern mining has been proposed to overcome the limitations of frequent pattern mining which cannot reflect the unique profits of items. High utility pattern mining has been actively conducted because it can find more valuable patterns than previous fields of pattern mining. However, its traditional approaches are designed to perform on the assumption that the data stored in databases is faultless. If there are unknown errors, such as noises, in a given database, the mining results traditional high utility pattern mining approaches mined in this database cannot be fully trusted. In this paper, a novel technique considering the noises is suggested in order to overcome this limitation. The proposed technique calculates the ranges of trustworthy utilities for patterns using a utility tolerance factor. By using this factor, the robust high utility patterns, called as approximate high utility patterns, can be extracted from a noisy database. To evaluate the performance of the proposed algorithm, various experiments are designed and conducted in terms of runtime, memory usage, and scalability. The experimental results show that the proposed algorithm outperforms than competitors, an apriori-based approach and UP-Growth.
format	Journal article
author	Yoonji Baek Unil Yun Heonho Kim Jongseong Kim Bay Vo Trương, Chí Tín Zhi-Hong Deng
author_facet	Yoonji Baek Unil Yun Heonho Kim Jongseong Kim Bay Vo Trương, Chí Tín Zhi-Hong Deng
author_sort	Yoonji Baek
title	Approximate high utility itemset mining in noisy environments
title_short	Approximate high utility itemset mining in noisy environments
title_full	Approximate high utility itemset mining in noisy environments
title_fullStr	Approximate high utility itemset mining in noisy environments
title_full_unstemmed	Approximate high utility itemset mining in noisy environments
title_sort	approximate high utility itemset mining in noisy environments
publisher	Elsevier B.V.
publishDate	2021
url	http://scholar.dlu.edu.vn/handle/123456789/591
_version_	1768305799522680832

Approximate high utility itemset mining in noisy environments

Những quyển sách tương tự