NDLI: FUZZSAM - visualization of fuzzy clustering results by modified Sammon mapping

Content Provider	IEEE Xplore Digital Library
Author	Abonyi, J. Babuska, R.
Copyright Year	2004
Description	Author affiliation: Dept. of Process Eng., Veszprem Univ., Hungary (Abonyi, J.)
Abstract	Since in practical data mining problems high-dimensional data are clustered, the resulting clusters are high-dimensional geometrical objects, which are difficult to analyze and interpret. Cluster validity measures try to solve this problem by providing a single numerical value. As a low dimensional graphical representation of the clusters could be much more informative than such a single value, this paper proposes a new tool for the visualization of fuzzy clustering results. By using the basic properties of fuzzy clustering algorithms, this new tool maps the cluster centers and the data such that the distances between the clusters and the data-points are preserved. During the iterative mapping process, the algorithm uses the membership values of the data and minimizes an objective function similar to the original clustering algorithm. Comparing to the original Sammon mapping not only reliable cluster shapes are obtained but the numerical complexity of the algorithm is also drastically reduced. The algorithm has been applied to several data sets and the numerical results show performance superior to principal component analysis and the classical Sammon mapping based projection. The examples demonstrate that proposed FUZZSAMM algorithm is a useful tool in user-guided clustering.
Starting Page	365
Ending Page	370
File Size	713667
Page Count	6
File Format	PDF
ISBN	0780383532
ISSN	10987584
DOI	10.1109/FUZZY.2004.1375750
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-07-25
Publisher Place	Hungary
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Data visualization Iterative algorithms Principal component analysis Prototypes Pollution measurement Data mining Data engineering Electronic mail Shape
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Theoretical Computer Science Software

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

Hybrid objective function of Fuzzy c-Varieties and cross-shape fuzzy cluster extraction

Linear Fuzzy Clustering of Mixed Databases Based on Cluster-wise Optimal Scaling of Categorical Variables

Clustering with unconstrained hyperboxes

A proposal of visualization method for obtaining interpretable fuzzy rules

Prototype-less Fuzzy Clustering

Linear fuzzy clustering of relational data based on extended Fuzzy c-Medoids

Cluster validation in k-Means clustering based on PCA-guided k-Means and procrustean transformation of PC scores

Fuzzy PCA-Guided Robust $k$-Means Clustering

A comparative study on cluster validity criteria in linear fuzzy clustering and pareto optimality analysis

FUZZSAM - visualization of fuzzy clustering results by modified Sammon mapping

Similar Documents

Hybrid objective function of Fuzzy c-Varieties and cross-shape fuzzy cluster extraction

Linear Fuzzy Clustering of Mixed Databases Based on Cluster-wise Optimal Scaling of Categorical Variables

Clustering with unconstrained hyperboxes

A proposal of visualization method for obtaining interpretable fuzzy rules

Prototype-less Fuzzy Clustering

Linear fuzzy clustering of relational data based on extended Fuzzy c-Medoids

Cluster validation in k-Means clustering based on PCA-guided k-Means and procrustean transformation of PC scores

Fuzzy PCA-Guided Robust $k$-Means Clustering

A comparative study on cluster validity criteria in linear fuzzy clustering and pareto optimality analysis

FUZZSAM - visualization of fuzzy clustering results by modified Sammon mapping