Title: A Review on Dynamic Clustering: Using Density Metrics

Author(s): Megha S.Mane1, Prof.N.R.Wankhade2


1PG student, Department of Computer Engineering, Late G.N.Sapkal College of Engineering, Nashik,

Maharashtra, India

This email address is being protected from spambots. You need JavaScript enabled to view it.

2Head and Associate Professor, Department of Computer Engineering, Late G.N.Sapkal College of Engineering, Nashik,

Maharashtra, India

This email address is being protected from spambots. You need JavaScript enabled to view it.

             


Abstract

Clustering of high dimensional dynamic data is challenging problem. Within the frame of big data analysis, the computational effort needed to perform the clustering task may become prohibitive and motivated the construction of several algorithms or the adaptation of existing ones, as the well known K-means algorithm. One of the critical problem in k-means, k-menoid, k-means or other clustering algorithms required to pre-assigned no. of k which cannot detect non-spherical clusters. With the existing RLClu algorithm needs users to pre-assign two minimum thresholds of the local density and the minimum density-based distance. Clustering is the process of data classification when none prior knowledge required for classification. To overcome these problems STClu clustering algorithm is proposed. In this algorithm a new metric is defined to evaluate the local density of each object, which shows better performance in distinguishing different objects. Furthermore, an outward statistical test method is used to identify the clustering centers automatically on a centrality metric constructed based on the new local density and new minimum density-based distance. Dynamic clustering is an approach to get and extract clusters in real time environments. It has much application such as, data warehousing, sensor network etc. Therefore there is need of such technique in which the data set is increasing in size over time by adding more and more data


License: This work is licensed under a Creative Commons Attribution 4.0 International License.

 

Website: http://www.ijecs.in

e-ISSN:  2319-724

Call For Paper

VOLUME 06. ISSUE 05 [May 2017]

IJECS invites authors to submit manuscripts Reporting original engineering research, computer science , original article, research article, case report, systematic reviews, or educational Innovations for publication for the Current issues. Types of manuscripts suitable for IJECS include: Engineering Science, Computer Science, Educational Innovation, Brief Report, Reviews on Teaching In keeping with high quality scholarship

Read More

Online Submission

If any difficulty you can also submit to: This email address is being protected from spambots. You need JavaScript enabled to view it..in

GettyImages.pngace.pngarx.pngcit.pngcomp.pngcross.pngdoaj.pngicn.pngino.pnglogo_wcmasthead_en.pngres.pngresbib.pngsci.pngscic.pngscics.pngulrich.pngur.png

FaceBook

About Us

The“International Journal of Engineering  and computer science”(IJECS™) is an international online journal in English published monthly. The aim of IJECS is to publish peer reviewed research and review articles in rapidly developing field of engineering science and technology

Address

Address : LIG 73 megdoot Nagar Mandsaur

State : Madhya Pradesh

Country : INDIA

Phone :+91 (822) 260 24 694

Email: editor@ijecs.in

Go to top