NDLI: Data clustering approaches survey and analysis

Content Provider	IEEE Xplore Digital Library
Author	Ahalya, G. Pandey, H.M.
Copyright Year	2015
Description	Author affiliation: CSE Dept., Amity Univ., Noida, India (Ahalya, G.; Pandey, H.M.)
Abstract	In the current world, there is a need to analyze and extract information from data. Clustering is one such analytical method which involves the distribution of data into groups of identical objects. Every group is known as a cluster, which consists of objects that have affinity within the cluster and disparity with the objects in other groups. This paper is intended to examine and evaluate various data clustering algorithms. The two major categories of clustering approaches are partition and hierarchical clustering. The algorithms which are dealt here are: k-means clustering algorithm, hierarchical clustering algorithm, density based clustering algorithm, self-organizing map algorithm, and expectation maximization clustering algorithm. All the mentioned algorithms are explained and analyzed based on the factors like the size of the dataset, type of the data set, number of clusters created, quality, accuracy and performance. This paper also provides the information about the tools which are used to implement the clustering approaches. The purpose of discussing the various software/tools is to make the beginners and new researchers to understand the working, which will help them to come up with new product and approaches for the improvement.
Starting Page	532
Ending Page	537
File Size	211517
Page Count	6
File Format	PDF
e-ISBN	9781479984336
DOI	10.1109/ABLAZE.2015.7154919
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-02-25
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Software algorithms Noise K-means clustering algorithm Partitioning algorithms Expectation maximization clustering algorithm Clustering Hierarchical clustering Accuracy Clustering algorithms Software Self-organization maps algorithm Density based clustering algorithm
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 optimized initialization center K-means clustering algorithm based on density

Improved varied density based spatial clustering algorithm with noise

Object Based Clustering Using Hybrid Algorithms

An Enhancement of K-means Clustering Algorithm

Parallel processing of enhanced K-means using OpenMP

Survey on text clustering algorithm

Combining Parallel Self-Organizing Maps and K-Means to Cluster Distributed Data

Clustering for high dimensional data

A Comparative Analysis of Clustering Algorithms

Data clustering approaches survey and analysis

Similar Documents

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

Improved varied density based spatial clustering algorithm with noise

Object Based Clustering Using Hybrid Algorithms

An Enhancement of K-means Clustering Algorithm

Parallel processing of enhanced K-means using OpenMP

Survey on text clustering algorithm

Combining Parallel Self-Organizing Maps and K-Means to Cluster Distributed Data

Clustering for high dimensional data

A Comparative Analysis of Clustering Algorithms

Data clustering approaches survey and analysis