research-article

Grid-based subspace clustering over data streams

Authors:

Won Suk LeeAuthors Info & Claims

CIKM '07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management

Pages 801 - 810

https://doi.org/10.1145/1321440.1321551

Published: 06 November 2007 Publication History

Abstract

A real-life data stream usually contains many dimensions and some dimensional values of its data elements may be missing. In order to effectively extract the on-going change of a data stream with respect to all the subsets of the dimensions of the data stream, a grid-based subspace clustering algorithm is proposed in this paper. Given an n-dimensional data stream, the on-going distribution statistics of data elements in each one-dimension data space is firstly monitored by a list of grid-cells called a sibling list. Once a dense grid-cell of a first-level sibling list becomes a dense unit grid-cell, new second-level sibling lists are created as its child nodes in order to trace any cluster in all possible two-dimensional rectangular subspaces. In such a way, a sibling tree grows up to the n^th level at most and a k-dimensional subcluster can be found in the k^th level of the sibling tree. The proposed method is comparatively analyzed by a series of experiments to identify its various characteristics.

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Cited By

Zhu WXiao RHuang RGong PZhang SDu X(2022)Efficient Gaussian Kernel Microcluster Real-Time Clustering Method for Industrial Internet of Things (IIoT) StreamsIEEE Internet of Things Journal10.1109/JIOT.2022.31793209:21(21323-21337)Online publication date: 1-Nov-2022
https://doi.org/10.1109/JIOT.2022.3179320
Liu HHelu XJin CLu HTian ZDu XAbualsaud K(2020)A Malware Detection Method for Health Sensor Data Based on Machine Learning2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT)10.1109/ICIoT48696.2020.9089478(277-282)Online publication date: Feb-2020
https://doi.org/10.1109/ICIoT48696.2020.9089478
Borutta FKazempour DMathy FKröger PSeidl T(2020)Detecting Arbitrarily Oriented Subspace Clusters in Data Streams Using Hough TransformAdvances in Knowledge Discovery and Data Mining10.1007/978-3-030-47426-3_28(356-368)Online publication date: 6-May-2020
https://doi.org/10.1007/978-3-030-47426-3_28
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Index Terms

Grid-based subspace clustering over data streams
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

CIKM '07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management

November 2007

1048 pages

ISBN:9781595938039

DOI:10.1145/1321440

Co-chair:
Alberto H. F. Laender,
Conference Chairs:
André O. Falcão
Universidade de Lisboa, Portugal
,
Øystein Haug Olsen,
General Chair:
Mário J. Silva
(Universidade de Lisboa, Portugal)
,
Program Chairs:
Ricardo Baeza-Yates,
Deborah L. McGuinness,
Bjorn Olstad

Copyright © 2007 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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Published: 06 November 2007

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November 6 - 10, 2007

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Zhu WXiao RHuang RGong PZhang SDu X(2022)Efficient Gaussian Kernel Microcluster Real-Time Clustering Method for Industrial Internet of Things (IIoT) StreamsIEEE Internet of Things Journal10.1109/JIOT.2022.31793209:21(21323-21337)Online publication date: 1-Nov-2022
https://doi.org/10.1109/JIOT.2022.3179320
Liu HHelu XJin CLu HTian ZDu XAbualsaud K(2020)A Malware Detection Method for Health Sensor Data Based on Machine Learning2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT)10.1109/ICIoT48696.2020.9089478(277-282)Online publication date: Feb-2020
https://doi.org/10.1109/ICIoT48696.2020.9089478
Borutta FKazempour DMathy FKröger PSeidl T(2020)Detecting Arbitrarily Oriented Subspace Clusters in Data Streams Using Hough TransformAdvances in Knowledge Discovery and Data Mining10.1007/978-3-030-47426-3_28(356-368)Online publication date: 6-May-2020
https://doi.org/10.1007/978-3-030-47426-3_28
Carnein MTrautmann H(2019)Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering AlgorithmsBusiness & Information Systems Engineering10.1007/s12599-019-00576-561:3(277-297)Online publication date: 21-Jan-2019
https://doi.org/10.1007/s12599-019-00576-5
Cao JLiang MLi YChen JLi HLiu RLiu J(2018)PCA-based hierarchical clustering of AIS trajectories with automatic extraction of clusters2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA)10.1109/ICBDA.2018.8367725(448-452)Online publication date: Mar-2018
https://doi.org/10.1109/ICBDA.2018.8367725
Wattanakitrungroj NManeeroj SLursinsap C(2017)Versatile Hyper-Elliptic Clustering Approach for Streaming Data Based on One-Pass-Thrown-Away LearningJournal of Classification10.1007/s00357-017-9222-134:1(108-147)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1007/s00357-017-9222-1
Xiang YGuohua LXiandong XLiandong L(2015)A data stream outlier detection algorithm based on gridThe 27th Chinese Control and Decision Conference (2015 CCDC)10.1109/CCDC.2015.7162657(4136-4141)Online publication date: May-2015
https://doi.org/10.1109/CCDC.2015.7162657
Hassani MKim YChoi SSeidl T(2015)Subspace clustering of data streamsJournal of Intelligent Information Systems10.1007/s10844-014-0319-245:3(319-335)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1007/s10844-014-0319-2
Sabau A(2013)Clustering Data Streams Using Mass EstimationProceedings of the 2013 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing10.1109/SYNASC.2013.45(289-295)Online publication date: 23-Sep-2013
https://dl.acm.org/doi/10.1109/SYNASC.2013.45
Hassani MKim YChoi SSeidl T(2013)Effective Evaluation Measures for Subspace Clustering of Data StreamsRevised Selected Papers of PAKDD 2013 International Workshops on Trends and Applications in Knowledge Discovery and Data Mining - Volume 786710.1007/978-3-642-40319-4_30(342-353)Online publication date: 14-Apr-2013
https://dl.acm.org/doi/10.1007/978-3-642-40319-4_30
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