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TitleDealing with repeated objects in SNNagg
Author(s)Galvão, João Rui Magalhães Velho da Cunha
Santos, Maribel Yasmina
Pires, João Moura
Costa, Carlos
KeywordsSpatial Data
Spatio-Temporal Data
Density-based Clustering
Issue date16-Feb-2016
JournalIAENG International Journal of Computer Science
CitationJoao Galvão, Maribel Yasmina Santos, Joao Moura Pires, and Carlos Costa, "Dealing with Repeated Objects in SNNagg", IAENG International Journal of Computer Science, vol. 43, no. 1, pp115-125, 2016, ISSN: 1819656X.
Abstract(s)Due to the constant technological advances and massive use of electronic devices, the amount of data generated has increased at a very high rate, leading to the urgent need to process larger amounts of data in less time. In order to be able to handle these large amounts of data, several techniques and algorithms have been developed in the area of knowledge discovery in databases, which process consists of several stages, including data mining that analyze vast amounts of data, identifying patterns, models or trends. Among the several data mining techniques, this work is focused in clustering spatial data with a density-based approach that uses the Shared Nearest Neighbor algorithm (SNN). SNN has shown several advantages when analyzing this type of data, identifying clusters of different sizes, shapes, and densities, and also dealing with noise. This paper presents and evaluates a new extension of SNN that is able to deal with repeated objects, creating aggregates that reduce the processing time required to cluster a given dataset, as repeated objects are excluded from the most time demanding step, which is associated with the identification of the k-nearest neighbors of a point. The proposed approach, SNNagg, was evaluated and the obtained results show that the processing time is reduced without compromising the quality of the obtained clusters.
AccessOpen access
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals

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