Show simple item record

dc.contributor.authorCallister, R.
dc.contributor.authorLazarescu, Mihai
dc.contributor.authorPham, DucSon
dc.date.accessioned2017-11-24T05:24:49Z
dc.date.available2017-11-24T05:24:49Z
dc.date.created2017-11-24T04:48:51Z
dc.date.issued2017
dc.identifier.citationCallister, R. and Lazarescu, M. and Pham, D. 2017. Graph-based clustering with DRepStream, pp. 850-857.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/58258
dc.identifier.doi10.1145/3019612.3019672
dc.description.abstract

© 2017 ACM. Finding and setting input parameters for clustering algorithms is a challenging thing due to the unsupervised nature of clustering. The accuracy of clustering algorithms can be affected greatly by setting parameters appropriately for the dataset, however without ground truth labels and external validation it can be impossible to know when the parameters are set well. In this paper we propose the DRepStream algorithm, which extends the RepStream algorithm. DRepStream uses a graph-based approach, and unlike its predecessor does not require the primary K parameter used in K-nearest neighbour graphs. Our algorithm automatically computes the number of outgoing edges for each vertex in the graph using a computed metric known as the anomalous edge score. We evaluate the performance of our algorithm on other previous stream clustering algorithms on real world benchmark datasets.

dc.titleGraph-based clustering with DRepStream
dc.typeConference Paper
dcterms.source.volumePart F128005
dcterms.source.startPage850
dcterms.source.endPage857
dcterms.source.titleProceedings of the ACM Symposium on Applied Computing
dcterms.source.seriesProceedings of the ACM Symposium on Applied Computing
dcterms.source.isbn9781450344869
curtin.departmentDepartment of Computing
curtin.accessStatusFulltext not available


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record