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2016 | OriginalPaper | Buchkapitel

A Novel Multi-view Similarity for Clustering Spatio-Temporal Data

verfasst von : Vijaya Bhaskar Velpula, M. H. M. Krishna Prasad

Erschienen in: Proceedings of the Second International Conference on Computer and Communication Technologies

Verlag: Springer India

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Abstract

With the enhanced usage of sensors and GPS devices, obtaining spatial and spatio-temporal data has become easy and analyses of these data in real-time applications are increasing day to day. Clustering is a data mining technique used for analyzing and obtaining unknown/hidden knowledge from the data/objects. Distance-based methods are helpful for analyzing and grouping the objects. In general, based on the type of data, Euclidean or Cosine distance-based techniques are used for grouping the data. Traditional techniques are point-based techniques and are based on single-view point, which may not produce efficient information and cannot be utilized for analyzing spatio-temporal objects. Hence, this paper presents a novel multi-view similarity technique for clustering spatio-temporal objects. Authors demonstrated the effectiveness of the proposed technique by adopting DBSCAN and implementing JDK1.2 on benchmarked datasets with respect to FMI indicator.

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Metadaten
Titel
A Novel Multi-view Similarity for Clustering Spatio-Temporal Data
verfasst von
Vijaya Bhaskar Velpula
M. H. M. Krishna Prasad
Copyright-Jahr
2016
Verlag
Springer India
DOI
https://doi.org/10.1007/978-81-322-2517-1_30