research-article

Mass estimation and its applications

Authors:

Guang-Tong Zhou,

James Swee Chuan TanAuthors Info & Claims

KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 989 - 998

https://doi.org/10.1145/1835804.1835929

Published: 25 July 2010 Publication History

Abstract

This paper introduces mass estimation--a base modelling mechanism in data mining. It provides the theoretical basis of mass and an efficient method to estimate mass. We show that it solves problems very effectively in tasks such as information retrieval, regression and anomaly detection. The models, which use mass in these three tasks, perform at least as good as and often better than a total of eight state-of-the-art methods in terms of task-specific performance measures. In addition, mass estimation has constant time and space complexities.

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Cited By

Liu TZhou ZYang L(2024)Layered isolation forest: A multi-level subspace algorithm for improving isolation forestNeurocomputing10.1016/j.neucom.2024.127525581(127525)Online publication date: May-2024
https://doi.org/10.1016/j.neucom.2024.127525
Kiersztyn APylak DHorodelski MKiersztyn KUrbanovich P(2024)Random clustering-based outlier detectorInformation Sciences: an International Journal10.1016/j.ins.2024.120498667:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.120498
Duan YLi CBai GChen GZhou FChen JGao ZZhang C(2023)MFGAD-INT: in-band network telemetry data-driven anomaly detection using multi-feature fusion graph deep learningJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-023-00492-w12:1Online publication date: 28-Aug-2023
https://dl.acm.org/doi/10.1186/s13677-023-00492-w
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Index Terms

Mass estimation and its applications
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning

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Published In

cover image ACM Conferences

KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining

July 2010

1240 pages

ISBN:9781450300551

DOI:10.1145/1835804

General Chairs:
Bharat Rao
Siemens
,
Balaji Krishnapuram
Siemens
,
Program Chairs:
Andrew Tomkins
Google Inc.
,
Qiang Yang
Hong Kong University of Science and Technology

Copyright © 2010 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 ACM 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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Publication History

Published: 25 July 2010

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mass estimation

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KDD '10

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KDD '10: The 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

July 25 - 28, 2010

DC, Washington, USA

Acceptance Rates

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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KDD '25

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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

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Cited By

Liu TZhou ZYang L(2024)Layered isolation forest: A multi-level subspace algorithm for improving isolation forestNeurocomputing10.1016/j.neucom.2024.127525581(127525)Online publication date: May-2024
https://doi.org/10.1016/j.neucom.2024.127525
Kiersztyn APylak DHorodelski MKiersztyn KUrbanovich P(2024)Random clustering-based outlier detectorInformation Sciences: an International Journal10.1016/j.ins.2024.120498667:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.120498
Duan YLi CBai GChen GZhou FChen JGao ZZhang C(2023)MFGAD-INT: in-band network telemetry data-driven anomaly detection using multi-feature fusion graph deep learningJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-023-00492-w12:1Online publication date: 28-Aug-2023
https://dl.acm.org/doi/10.1186/s13677-023-00492-w
Wang JWu YLi SNie F(2023)A self-training algorithm based on the two-stage data editing method with mass-based dissimilarityNeural Networks10.1016/j.neunet.2023.09.046168(431-449)Online publication date: Nov-2023
https://doi.org/10.1016/j.neunet.2023.09.046
Liu FWang YLi ZGuan HXie G(2023) S: Adaptive anomaly detection on sporadic data streams Computer Communications10.1016/j.comcom.2023.06.027209(151-162)Online publication date: Sep-2023
https://doi.org/10.1016/j.comcom.2023.06.027
Syriopoulos PKalampalikis NKotsiantis SVrahatis M(2023)kNN Classification: a reviewAnnals of Mathematics and Artificial Intelligence10.1007/s10472-023-09882-xOnline publication date: 1-Sep-2023
https://doi.org/10.1007/s10472-023-09882-x
Sun XQu YGao LSun XQi HZhang BShen T(2022)Ensemble-Based Information Retrieval With Mass Estimation for Hyperspectral Target DetectionIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2021.307558360(1-23)Online publication date: 2022
https://doi.org/10.1109/TGRS.2021.3075583
Kiersztyn AKarczmarek PKiersztyn KPedrycz W(2022)Detection and Classification of Anomalies in Large Datasets on the Basis of Information GranulesIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2021.307626530:8(2850-2860)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1109/TFUZZ.2021.3076265
Karczmarek PGalka LDolecki MPedrycz WCzerwinski DKiersztyn AStegierski R(2022)Enhanced Tree-Based Anomaly Detection2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE55066.2022.9882557(1-7)Online publication date: 18-Jul-2022
https://doi.org/10.1109/FUZZ-IEEE55066.2022.9882557
Karczmarek PKiersztyn APedrycz WCzerwiński D(2022)Fuzzy C-Means-based Isolation ForestApplied Soft Computing10.1016/j.asoc.2021.107354106:COnline publication date: 9-Apr-2022
https://dl.acm.org/doi/10.1016/j.asoc.2021.107354
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