NDLI: Tree model guided candidate generation for mining frequent subtrees from XML documents

Please wait, while we are loading the content...

Tree model guided candidate generation for mining frequent subtrees from XML documents

Content Provider	ACM Digital Library
Author	Dillon, Tharam S. Hadzic, Fedja Feng, Ling Chang, Elizabeth Tan, Henry
Copyright Year	2008
Abstract	Due to the inherent flexibilities in both structure and semantics, XML association rules mining faces few challenges, such as: a more complicated hierarchical data structure and ordered data context. Mining frequent patterns from XML documents can be recast as mining frequent tree structures from a database of XML documents. In this study, we model a database of XML documents as a database of rooted labeled ordered subtrees. In particular, we are mainly concerned with mining frequent induced and embedded ordered subtrees. Our main contributions are as follows. We describe our unique embedding list representation of the tree structure, which enables efficient implementation of our Tree Model Guided $(\textit{TMG})$ candidate generation. $\textit{TMG}$ is an optimal, nonredundant enumeration strategy that enumerates all the valid candidates that conform to the structural aspects of the data. We show through a mathematical model and experiments that $\textit{TMG}$ has better complexity compared to the commonly used join approach. In this article, we propose two algorithms, MB3-Miner and iMB3-Miner. MB3-Miner mines embedded subtrees. iMB3-Miner mines induced and/or embedded subtrees by using the maximum level of embedding constraint. Our experiments with both synthetic and real datasets against two well-known algorithms for mining induced and embedded subtrees, demonstrate the effectiveness and the efficiency of the proposed techniques.
Starting Page	1
Ending Page	43
Page Count	43
File Format	PDF
ISSN	15564681
e-ISSN	1556472X
DOI	10.1145/1376815.1376818
Volume Number	2
Issue Number	2
Journal	ACM Transactions on Knowledge Discovery from Data (TKDD)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-07-24
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	FREQT TMG Tree mining TreeMiner Tree model guided
Content Type	Text
Resource Type	Article
Subject	Computer Science

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

MB3 Miner: mining eMBedded sub-TREEs using Tree Model Guided candidate generation (2005)

Tree model guided (TMG) enumeration as the basis for mining frequent patterns from XML documents

UNI3 - efficient algorithm for mining unordered induced subtrees using TMG candidate generation

U3 - Mning Unordered Embedded Subtrees Using TMG Candidate Generation

U3 - Mning Unordered Embedded Subtrees Using TMG Candidate Generation

Efficient data mining for maximal frequent subtrees (2003)

Complete Discovery of Weighted Frequent Subtrees in Tree-Structured (2006)

MB3-Miner: mining eMBedded subTREEs using Tree Model Guided candidate generation

IMB3 Miner: Mining Induced/Embedded Subtrees by Constraining the Level of Embedding (2006)

Tree model guided candidate generation for mining frequent subtrees from XML documents

Similar Documents

MB3 Miner: mining eMBedded sub-TREEs using Tree Model Guided candidate generation (2005)

Tree model guided (TMG) enumeration as the basis for mining frequent patterns from XML documents

UNI3 - efficient algorithm for mining unordered induced subtrees using TMG candidate generation

U3 - Mning Unordered Embedded Subtrees Using TMG Candidate Generation

U3 - Mning Unordered Embedded Subtrees Using TMG Candidate Generation

Efficient data mining for maximal frequent subtrees (2003)

Complete Discovery of Weighted Frequent Subtrees in Tree-Structured (2006)

MB3-Miner: mining eMBedded subTREEs using Tree Model Guided candidate generation

IMB3 Miner: Mining Induced/Embedded Subtrees by Constraining the Level of Embedding (2006)

Tree model guided candidate generation for mining frequent subtrees from XML documents