An Efficient and Scalable Density-based Clustering Algorithm for Normalize Data

Data clustering is a method of putting same data object into group. A clustering rule does partitions of a data set into many groups supported the principle of maximizing the intra-class similarity and minimizing the inter-class similarity. Finding clusters in object, particularly high dimensional o...

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Bibliographic Details
Published in:	Procedia computer science Vol. 92; pp. 136 - 141
Main Authors:	Nidhi, Patel, Km Archana
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 2016
Subjects:	Computational complexity Density based Clustering Algorithm Computational complexity Density based Clustering Algorithm
ISSN:	1877-0509, 1877-0509
Online Access:	Get full text
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Summary:	Data clustering is a method of putting same data object into group. A clustering rule does partitions of a data set into many groups supported the principle of maximizing the intra-class similarity and minimizing the inter-class similarity. Finding clusters in object, particularly high dimensional object, is difficult when the clusters are different shapes, sizes, and densities, and when data contains noise and outliers. This paper provides a new clustering algorithm for normalized data set and proven that our new planned clustering approach work efficiently when dataset are normalized.
ISSN:	1877-0509 1877-0509
DOI:	10.1016/j.procs.2016.07.336