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  1. Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA '13)
  2. Randomly sampling maximal itemsets
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Interactive visual analytics for high dimensional data
Building blocks for exploratory data analysis tools
Methods for exploring and mining tables on Wikipedia
One click mining: interactive local pattern discovery through implicit preference and performance learning
Lytic: synthesizing high-dimensional algorithmic analysis with domain-agnostic, faceted visual analytics
A process-centric data mining and visual analytic tool for exploring complex social networks
Zips: mining compressing sequential patterns in streams
Augmenting MATLAB with semantic objects for an interactive visual environment
Online spatial data analysis and visualization system
Randomly sampling maximal itemsets
Towards anytime active learning: interrupting experts to reduce annotation costs
Storygraph: extracting patterns from spatio-temporal data

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Randomly sampling maximal itemsets

Content Provider ACM Digital Library
Author Goethals, Bart Moens, Sandy
Abstract Pattern mining techniques generally enumerate lots of uninteresting and redundant patterns. To obtain less redundant collections, techniques exist that give condensed representations of these collections. However, the proposed techniques often rely on complete enumeration of the pattern space, which can be prohibitive in terms of time and memory. Sampling can be used to filter the output space of patterns without explicit enumeration. We propose a framework for random sampling of maximal itemsets from transactional databases. The presented framework can use any monotonically decreasing measure as interestingness criteria for this purpose. Moreover, we use an approximation measure to guide the search for maximal sets to different parts of the output space. We show in our experiments that the method can rapidly generate small collections of patterns with good quality. The sampling framework has been implemented in the interactive visual data mining tool called $MIME^{1},$ as such enabling users to quickly sample a collection of patterns and analyze the results.
Starting Page 79
Ending Page 86
Page Count 8
File Format PDF
ISBN 9781450323291
DOI 10.1145/2501511.2501523
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-08-11
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Random walks Output space sampling Maximal itemsets
Content Type Text
Resource Type Article
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