NDLI: Mining Frequent Independent Patterns and Frequent Correlated Patterns Synchronously

Content Provider	IEEE Xplore Digital Library
Author	Zhongmei Zhou
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Zhangzhou Normal Univ., Zhangzhou (Zhongmei Zhou)
Abstract	One of the main tasks of KDTCM (knowledge discovery in Traditional Chinese Medicine) is discovering novel paired or grouped drugs in Chinese Medical Formula (CMF) database, which are special combinations of two or more drugs. Correlation mining is much effective because of the large number of correlation relationships among various kinds of drugs. However, an independent pattern might have much more probability than a correlated pattern to be a novel paired or grouped drug even if they have same support for the sake of the downward closure property of independence. Therefore, we mine frequent independent patterns and frequent correlated patterns synchronously. Notions of an independent pattern and a correlated pattern are given. An algorithm is developed for mining frequent independent patterns and frequent correlated patterns synchronously. Experimental results show the effectiveness and the correctness of the techniques developed in this paper. Experimental results also show the great necessity of mining independent patterns and correlated patterns synchronously.
Starting Page	552
Ending Page	556
File Size	291185
Page Count	5
File Format	PDF
ISBN	9780769533056
DOI	10.1109/FSKD.2008.27
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Drugs Computer science Knowledge engineering Databases Probability Data engineering Synchronous generators Electronic mail Fuzzy systems Biomedical engineering
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

VTK: Vertical Mining of Top-Rank-K Frequent Patterns

Mining Top-Rank-K Frequent Patterns

Mining Positive and Negative Fuzzy Sequential Patterns in Large Transaction Databases

Mining Maximal Sequential Patterns

A mining algorithm for fuzzy weighted association rules

Mining Strongly Associated Rules

Mining Short Association Rules from Large Database

Mining fuzzy sequential patterns from quantitative data

Fuzzy spatial data mining

Mining Frequent Independent Patterns and Frequent Correlated Patterns Synchronously

Similar Documents

VTK: Vertical Mining of Top-Rank-K Frequent Patterns

Mining Top-Rank-K Frequent Patterns

Mining Positive and Negative Fuzzy Sequential Patterns in Large Transaction Databases

Mining Maximal Sequential Patterns

A mining algorithm for fuzzy weighted association rules

Mining Strongly Associated Rules

Mining Short Association Rules from Large Database

Mining fuzzy sequential patterns from quantitative data

Fuzzy spatial data mining

Mining Frequent Independent Patterns and Frequent Correlated Patterns Synchronously