Fast SNN-Based Clustering Approach for Large Geospatial Data Sets

Antunes, Arménio António Fernandes; Santos, Maribel Yasmina; Moreira, Adriano

doi:10.1007/978-3-319-03611-3_11

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…The implementation used to perform clustering with the SNN algorithm was the F-SNN [17]. To speed up the experiments, several scripts were created to run batch SNN executions with cycles that test all possible combinations within a pre-determined range.…”

Section: A Methodologymentioning

confidence: 99%

SNN Input Parameters: How Are They Related?

Moreira

Santos

Pires

2013

2013 International Conference on Parallel and Distributed Systems

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Nowadays, organizations are facing several challenges when they try to analyze generated data with the aim of extracting useful information. This analytical capacity needs to be enhanced with tools capable of dealing with big data sets without making the analytical process a difficult task. Clustering is usually used, as this technique does not require any prior knowledge about the data. However, clustering algorithms usually require one or more input parameters that influence the clustering process and the results that can be obtained. This work analyses the relation between the three input parameters of the SNN (Shared Nearest Neighbor) algorithm and proposes specific guidelines for the identification of the appropriate input parameters that optimizes the processing time.

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Section: A Methodologymentioning

confidence: 99%

SNN Input Parameters: How Are They Related?

Moreira

Santos

Pires

2013

2013 International Conference on Parallel and Distributed Systems

Self Cite

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“…The implementation used to perform clustering with the SNN algorithm was the F-SNN (A. A. F. Antunes, 2013;A. Antunes, Santos, & Moreira, 2014).…”

Section: Adopted Methodologymentioning

confidence: 99%

Understanding the SNN Input Parameters and How They Affect the Clustering Results

Moreira

Santos

Pires

et al. 2015

International Journal of Data Warehousing and Mining

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Huge amounts of data are available for analysis in nowadays organizations, which are facing several challenges when trying to analyze the generated data with the aim of extracting useful information. This analytical capability needs to be enhanced with tools capable of dealing with big data sets without making the analytical process an arduous task. Clustering is usually used in the data analysis process, as this technique does not require any prior knowledge about the data. However, clustering algorithms usually require one or more input parameters that influence the clustering process and the results that can be obtained. This work analyses the relation between the three input parameters of the SNN (Shared Nearest Neighbor) clustering algorithm, providing a comprehensive understanding of the relationships that were identified between k, Eps and MinPts, the algorithm's input parameters. Moreover, this work also proposes specific guidelines for the definition of the appropriate input parameters, optimizing the processing time, as the number of trials needed to achieve appropriate results can be substantial reduced.

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