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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Vydáno v:Procedia computer science Ročník 92; s. 136 - 141
Hlavní autoři: Nidhi, Patel, Km Archana
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 2016
Témata:
Computational complexity
Density based Clustering Algorithm
Computational complexity
Density based Clustering Algorithm
ISSN:1877-0509, 1877-0509
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Shrnutí: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