Article

A new OLAP aggregation based on the AHC technique

Authors:

Riadh Ben Messaoud,

Sabine RabasédaAuthors Info & Claims

DOLAP '04: Proceedings of the 7th ACM international workshop on Data warehousing and OLAP

Pages 65 - 72

https://doi.org/10.1145/1031763.1031777

Published: 12 November 2004 Publication History

Abstract

Nowadays, decision support systems are evolving in order to handle complex data. Some recent works have shown the interest of combining on-line analysis processing (OLAP) and data mining. We think that coupling OLAP and data mining would provide excellent solutions to treat complex data. To do that, we propose an enhanced OLAP operator based on the agglomerative hierarchical clustering (AHC). The here proposed operator, called <i>OpAC</i> (Operator for Aggregation by Clustering) is able to provide significant aggregates of facts refereed to complex objects. We complete this operator with a tool allowing the user to evaluate the best partition from the AHC results corresponding to the most interesting aggregates of facts.

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Cited By

Sautot LBimonte SJournaux L(2020)A Semi-Automatic Design Methodology for (Big) Data Warehouse Transforming Facts into DimensionsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.292562133:1(28-42)Online publication date: 7-Dec-2020
https://dl.acm.org/doi/10.1109/TKDE.2019.2925621
Usman M(2018)Integration of Data Mining and Statistical Methods for Constructing and Exploring Data CubesInformation Retrieval and Management10.4018/978-1-5225-5191-1.ch037(871-882)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-5191-1.ch037
Bimonte SSautot LJournaux LFaivre B(2017)Multidimensional Model Design using Data MiningInternational Journal of Data Warehousing and Mining10.5555/3077757.307775813:1(1-35)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.5555/3077757.3077758
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Index Terms

A new OLAP aggregation based on the AHC technique
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
      2. Unsupervised learning
        Cluster analysis
    2. Machine learning approaches
      1. Classification and regression trees
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Multimedia information systems
      1. Multimedia databases

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cover image ACM Conferences

DOLAP '04: Proceedings of the 7th ACM international workshop on Data warehousing and OLAP

November 2004

130 pages

ISBN:1581139772

DOI:10.1145/1031763

General Chair:
Il-Yeol Song
Drexel University
,
Program Chair:
Karen Davis
University of Cincinnati

Copyright © 2004 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: 12 November 2004

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CIKM04

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CIKM04: Conference on Information and Knowledge Management

November 12 - 13, 2004

DC, Washington, USA

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Overall Acceptance Rate 29 of 79 submissions, 37%

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Cited By

Sautot LBimonte SJournaux L(2020)A Semi-Automatic Design Methodology for (Big) Data Warehouse Transforming Facts into DimensionsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.292562133:1(28-42)Online publication date: 7-Dec-2020
https://dl.acm.org/doi/10.1109/TKDE.2019.2925621
Usman M(2018)Integration of Data Mining and Statistical Methods for Constructing and Exploring Data CubesInformation Retrieval and Management10.4018/978-1-5225-5191-1.ch037(871-882)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-5191-1.ch037
Bimonte SSautot LJournaux LFaivre B(2017)Multidimensional Model Design using Data MiningInternational Journal of Data Warehousing and Mining10.5555/3077757.307775813:1(1-35)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.5555/3077757.3077758
Bimonte SSautot LJournaux LFaivre B(2017)Multidimensional Model Design using Data MiningInternational Journal of Data Warehousing and Mining10.4018/IJDWM.201701010113:1(1-35)Online publication date: Jan-2017
https://doi.org/10.4018/IJDWM.2017010101
Usman M(2015)Integration of Data Mining and Statistical Methods for Constructing and Exploring Data CubesImproving Knowledge Discovery through the Integration of Data Mining Techniques10.4018/978-1-4666-8513-0.ch001(1-12)Online publication date: 2015
https://doi.org/10.4018/978-1-4666-8513-0.ch001
Leonhardi BMitschang BPulido RSieb CWurst M(2010)Augmenting OLAP exploration with dynamic advanced analyticsProceedings of the 13th International Conference on Extending Database Technology10.1145/1739041.1739127(687-692)Online publication date: 22-Mar-2010
https://dl.acm.org/doi/10.1145/1739041.1739127
Usman MAsghar SFong S(2010)Data mining and automatic OLAP schema generation2010 Fifth International Conference on Digital Information Management (ICDIM)10.1109/ICDIM.2010.5664622(35-43)Online publication date: Jul-2010
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Ravat FTeste OTournier RZurfluh G(2010)Finding an application-appropriate model for XML data warehousesInformation Systems10.1016/j.is.2009.12.00235:6(662-687)Online publication date: 1-Sep-2010
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Voicu MDragota MTurcu RBânciu AMircea GSosea D(2009)Analyses and algorithms for exploring relational databasesProceedings of the WSEAES 13th international conference on Computers10.5555/1627695.1627768(386-393)Online publication date: 23-Jul-2009
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Usman MAsghar SFong S(2009)A Conceptual Model for Combining Enhanced OLAP and Data Mining SystemsProceedings of the 2009 Fifth International Joint Conference on INC, IMS and IDC10.1109/NCM.2009.354(1958-1963)Online publication date: 25-Aug-2009
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