NDLI: An integrated clustering approach for high dimensional categorical data

Content Provider	IEEE Xplore Digital Library
Author	Kalaivani, K. Raghavendra, A.P.V.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci. & Eng., VSB Eng. Coll., Karur, India (Kalaivani, K.; Raghavendra, A.P.V.)
Abstract	Clustering is an attractive and important task in data mining which is used in many applications. However earlier work on clustering focused on only categorical data which is based on attribute values for grouping similar kind of data items thus will leads to convergence problem of clustering process. This proposed work is to enhance the existing k-means clustering process based on the categorical and mixed data types in efficient manner. The goal is to use integrated clustering approach based on high dimensional categorical data that works well for data with mixed continuous and categorical features. The experimental results of the proposed method on several data sets are suggest that the link based cluster ensemble algorithm integrate with proposed k-means algorithm to produce accurate clustering results. In this proposed algorithm prove the convergence property of clustering process, thus will improve the accuracy of clustering results. The scope of this proposed work is used to provide the accurate and efficient results, whenever the user wants to access the data from the database.
Sponsorship	IEEE Madras Sect.
Starting Page	1
Ending Page	4
File Size	412554
Page Count	4
File Format	PDF
ISBN	9781467325929
e-ISBN	9781467325943
DOI	10.1109/ICGHPC.2013.6533920
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-03-14
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Link-based Cluster Ensemble Accuracy Machine learning algorithms Categorical Data Clustering algorithms Partitioning algorithms Data mining Clustering Proposed K-Means Mixed Data Convergence
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

An efficient clustering algorithm based on Z-Score ranking method

Measurement of similarity using link based cluster approach for categorical data

An optimized initialization center K-means clustering algorithm based on density

Clustering for high dimensional data

A Link-Based Cluster Ensemble Approach for Categorical Data Clustering

A k-Means-Based Projected Clustering Algorithm

Data clustering with modified K-means algorithm

Efficient ensemble algorithm for mixed numeric and categorical data

An improved k-means clustering algorithm

An integrated clustering approach for high dimensional categorical data

Similar Documents

An efficient clustering algorithm based on Z-Score ranking method

Measurement of similarity using link based cluster approach for categorical data

An optimized initialization center K-means clustering algorithm based on density

Clustering for high dimensional data

A Link-Based Cluster Ensemble Approach for Categorical Data Clustering

A k-Means-Based Projected Clustering Algorithm

Data clustering with modified K-means algorithm

Efficient ensemble algorithm for mixed numeric and categorical data

An improved k-means clustering algorithm

An integrated clustering approach for high dimensional categorical data