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short-paper

Interpretable Mining of Influential Patterns from Sparse Web

Authors:

Connor C.J. Hryhoruk,

Carson K. LeungAuthors Info & Claims

WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

Pages 532 - 537

https://doi.org/10.1145/3486622.3494008

Published: 13 April 2022 Publication History

Abstract

Big data are everywhere. World Wide Web is an example of these big data. It has become a vast data production and consumption platform, at which threads of data evolve from multiple devices, by different human interactions, over worldwide locations, under divergent distributed settings. Embedded in these big web data is implicit, previously unknown and potentially useful information and knowledge that awaited to be discovered. This calls for web intelligence solutions, which make good use of data science and data mining (especially, web mining) to discover useful knowledge and important information from the web. As a web mining task, web structure mining aims to examine incoming and outgoing links on web pages and make recommendations of frequently referenced web pages to web surfers. As another web mining task, web usage mining aims to examine web surfer patterns and make recommendations of frequently visited pages to web surfers. While the size of the web is huge, the connection among all web pages may be sparse. In other words, the number of vertex nodes (i.e., web pages) on the web is huge, the number of directed edges (i.e., incoming and outgoing hyperlinks between web pages) may be small. This leads to a sparse web. In this paper, we present a solution for interpretable mining of influential patterns from sparse web. In particular, we represent web structure and usage information by bitmaps to capture connections to web pages. Due to the sparsity of the web, we compress the bitmaps, and use them in mining influential patterns (e.g., popular web pages). For explainability of the mining process, we ensure the compressed bitmaps are interpretable. Evaluation on real-life web data demonstrates the effectiveness, interpretability and practicality of our solution for interpretable mining of influential patterns from sparse web.

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Cited By

Goenka A(2024)Developing a Big Data Science Based Model Linked to Meteorological Data for Enhanced Applicability of Transportation AnalyticsInternational Journal of Professional Studies10.37648/ijps.v17i01.02017:1(256-266)Online publication date: 2024
https://doi.org/10.37648/ijps.v17i01.020
Hryhoruk CLeung C(2022)Web Mining from Interpretable Compressed Representation of Sparse Web2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT55865.2022.00097(620-627)Online publication date: Nov-2022
https://doi.org/10.1109/WI-IAT55865.2022.00097
Leung CMadill ESouza JZhang C(2022)Towards Trustworthy Artificial Intelligence in Healthcare2022 IEEE 10th International Conference on Healthcare Informatics (ICHI)10.1109/ICHI54592.2022.00127(626-632)Online publication date: Jun-2022
https://doi.org/10.1109/ICHI54592.2022.00127
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cover image ACM Conferences

WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

December 2021

698 pages

ISBN:9781450391153

DOI:10.1145/3486622

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 13 April 2022

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WI-IAT '21

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SIGAI

WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence

December 14 - 17, 2021

VIC, Melbourne, Australia

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Cited By

Goenka A(2024)Developing a Big Data Science Based Model Linked to Meteorological Data for Enhanced Applicability of Transportation AnalyticsInternational Journal of Professional Studies10.37648/ijps.v17i01.02017:1(256-266)Online publication date: 2024
https://doi.org/10.37648/ijps.v17i01.020
Hryhoruk CLeung C(2022)Web Mining from Interpretable Compressed Representation of Sparse Web2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT55865.2022.00097(620-627)Online publication date: Nov-2022
https://doi.org/10.1109/WI-IAT55865.2022.00097
Leung CMadill ESouza JZhang C(2022)Towards Trustworthy Artificial Intelligence in Healthcare2022 IEEE 10th International Conference on Healthcare Informatics (ICHI)10.1109/ICHI54592.2022.00127(626-632)Online publication date: Jun-2022
https://doi.org/10.1109/ICHI54592.2022.00127
Thong Tran NLeung CMadill EBinh P(2022)A Deep Learning Based Predictive Model for Healthcare Analytics2022 IEEE 10th International Conference on Healthcare Informatics (ICHI)10.1109/ICHI54592.2022.00106(547-549)Online publication date: Jun-2022
https://doi.org/10.1109/ICHI54592.2022.00106
Dhaliwal BImran RLeung CR. Madill E(2022)Trustworthy Explanations for Knowledge Discovered from E-Health Records2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)10.1109/HealthCom54947.2022.9982786(246-251)Online publication date: 17-Oct-2022
https://doi.org/10.1109/HealthCom54947.2022.9982786
Nguyen BShinnie MLeung CKuzie MKokilev NKaur S(2022)A Data Science Solution for Mining Weather Data and Transportation Data for Smart Cities2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC54236.2022.00266(1672-1677)Online publication date: Jun-2022
https://doi.org/10.1109/COMPSAC54236.2022.00266
Boch KKristjanson FLeung CLiu YUnrau AZhang SCuzzocrea A(2022)Mining Popular Topics from the Media2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC54236.2022.00199(1262-1267)Online publication date: Jun-2022
https://doi.org/10.1109/COMPSAC54236.2022.00199
Bouttell ALee SLeung CLevesque MSon SZhang WCuzzocrea A(2022)Predictive Analytics for Supporting Environmental Sustainability and Disaster Management2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC54236.2022.00198(1256-1261)Online publication date: Jun-2022
https://doi.org/10.1109/COMPSAC54236.2022.00198
Kaur SKokilev NKuzie MLeung CNguyen BPazdor AShinnie M(2022)A Big Data Science Solution for Transportation Analytics with Meteorological Data2022 IEEE 16th International Conference on Big Data Science and Engineering (BigDataSE)10.1109/BigDataSE56411.2022.00013(21-28)Online publication date: Dec-2022
https://doi.org/10.1109/BigDataSE56411.2022.00013

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