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FpViz: a visualizer for frequent pattern mining

Authors:

Carson Kai-Sang Leung,

Christopher L. CarmichaelAuthors Info & Claims

VAKD '09: Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration

Pages 30 - 39

https://doi.org/10.1145/1562849.1562853

Published: 28 June 2009 Publication History

Abstract

Over the past 15 years, numerous algorithms have been proposed for frequent pattern mining as it plays an essential role in many knowledge discovery and data mining (KDD) tasks. Most of these frequent pattern mining algorithms return the mined results in the form of textual lists containing frequent patterns showing those frequently occurring sets of items. It is well known that "a picture is worth a thousand words". The use of visual representation can enhance the user understanding of the inherent relations in a collection of frequent patterns. A few visualizers have been developed to visualize the input data or the mined results. However, most of these visualizers were not designed for visualizing the mined frequent patterns. In this paper, we develop a visualizer for frequent pattern mining. Such a visualizer---called FpViz---gives users an insight about the data, allows them to zoom in and zoom out, and provides details on demand. Moreover, FpViz is also equipped with several interactive features for effective visual support in the data analysis and KDD process for various real-life applications.

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FpViz: a visualizer for frequent pattern mining

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cover image ACM Conferences

VAKD '09: Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration

June 2009

92 pages

ISBN:9781605586700

DOI:10.1145/1562849

General Chairs:
Kai Puolamäki
Helsinki Institute for Information Technology HIIT
,
Heikki Mannila
Helsinki Institute for Information Technology HIIT
,
Alessio Bertone
Danube University Krems
,
Silvia Miksch
Danube University Krems

Copyright © 2009 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]

Sponsors

SIGMOD: ACM Special Interest Group on Management of Data
VisMaster, a European FP7 Coordination Action Project focused on Visual Analytics
PASCAL2 - Pattern Analysis, Statistical Modelling and Computational Learning
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
National Visualization and Analytics Center (NVAC)
Helsinki Institute for Information Technology HIIT
Danube University Krems, Departement of Information and Knowledge Engineering (DUK)

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Association for Computing Machinery

New York, NY, United States

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Published: 28 June 2009

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KDD09: The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

July 28, 2009

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https://doi.org/10.1109/IUCC-CIT-DSCI-SmartCNS55181.2021.00055
Leung CWen QZhao C(2021)Big Data Mining on Health Informatics Data for Cities2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)10.1109/HPCC-DSS-SmartCity-DependSys53884.2021.00253(1720-1727)Online publication date: Dec-2021
https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys53884.2021.00253
Shang SLeung CChen YPazdor A(2020)Spatial Data Science of COVID-19 Data2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)10.1109/HPCC-SmartCity-DSS50907.2020.00177(1370-1375)Online publication date: Dec-2020
https://doi.org/10.1109/HPCC-SmartCity-DSS50907.2020.00177
Leung CChen YShang SDeng D(2020)Big Data Science on COVID-19 Data2020 IEEE 14th International Conference on Big Data Science and Engineering (BigDataSE)10.1109/BigDataSE50710.2020.00010(14-21)Online publication date: Dec-2020
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Singh SLeung CAlhajj R(2020)A theoretical approach for discovery of friends from directed social graphsProceedings of the 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1109/ASONAM49781.2020.9381341(697-701)Online publication date: 7-Dec-2020
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Leung CCarmichael CJohnstone PXing RYuen D(2017)Interactive Visual Analytics of Big DataOntologies and Big Data Considerations for Effective Intelligence10.4018/978-1-5225-2058-0.ch001(1-26)Online publication date: 2017
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Dautriche RTermier ABlanch RSantana M(2016)Towards Visualizing Hidden Structures2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2016.0171(1183-1190)Online publication date: Dec-2016
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