NDLI: Assessing the Significance of Data Mining Results on Graphs with Feature Vectors

Content Provider	IEEE Xplore Digital Library
Author	Gunnemann, S. Phuong Dao Jamali, M. Ester, M.
Copyright Year	2012
Abstract	Assessing the significance of data mining results is an important step in the knowledge discovery process. While results might appear interesting at a first glance, they can often be explained by already known characteristics of the data. Randomization is an established technique for significance testing, and methods to assess data mining results on vector data or network data have been proposed. In many applications, however, both sources are simultaneously given. Since these sources are rarely independent of each other but highly correlated, naively applying existing randomization methods on each source separately is questionable. In this work, we present a method to assess the significance of mining results on graphs with binary features vectors. We propose a novel null model that preserves correlation information between both sources. Our randomization exploits an adaptive Metropolis sampling and interweaves attribute randomization and graph randomization steps. In thorough experiments, we demonstrate the application of our technique. Our results indicate that while simultaneously using both sources is beneficial, often one source of information is dominant for determining the mining results.
Starting Page	270
Ending Page	279
File Size	484681
Page Count	10
File Format	PDF
ISBN	9781467346498
ISSN	15504786
DOI	10.1109/ICDM.2012.70
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-10
Publisher Place	Belgium
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Data mining Vectors Data models Testing Markov processes Clustering algorithms data mining graph network significance testing randomization
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Assessing Data Mining Results on Matrices with Randomization

Assessing data mining results via swap randomization

Graph summarization for attributed graphs

Randomization techniques for graphs (2009)

Mining and Modeling Processes on Graphs

Relevance of Negative Links in Graph Partitioning: A Case Study Using Votes from the European Parliament

Randomization techniques for graphs (2009)

Incremental learning of full body motion primitives for humanoid robots

Node-Pair Feature Extraction for Link Prediction

Assessing the Significance of Data Mining Results on Graphs with Feature Vectors

Similar Documents

Assessing Data Mining Results on Matrices with Randomization

Assessing data mining results via swap randomization

Graph summarization for attributed graphs

Randomization techniques for graphs (2009)

Mining and Modeling Processes on Graphs

Relevance of Negative Links in Graph Partitioning: A Case Study Using Votes from the European Parliament

Randomization techniques for graphs (2009)

Incremental learning of full body motion primitives for humanoid robots

Node-Pair Feature Extraction for Link Prediction

Assessing the Significance of Data Mining Results on Graphs with Feature Vectors