Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Daniyal Kazempour"'
Publikováno v:
Data Science and Engineering, Vol 5, Iss 4, Pp 360-374 (2020)
Abstract OPTICS is a popular tool to analyze the clustering structure of a dataset visually. The created two-dimensional plots indicate very dense areas and cluster candidates in the data as troughs. Each horizontal slice represents an outcome of a d
Externí odkaz:
https://doaj.org/article/7c5031f405f446c684602f3c38e3ffb3
Publikováno v:
PLoS ONE, Vol 9, Iss 2, p e89420 (2014)
Lanthipeptides are a class of ribosomally synthesised and post-translationally modified peptide (RiPP) natural products from the bacterial secondary metabolism. Their name is derived from the characteristic lanthionine or methyl-lanthionine residues
Externí odkaz:
https://doaj.org/article/eaf9dd6b3d8243299c9a46349ed7cc86
Publikováno v:
Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::370cd03b0a0866ff5b55b39846152a78
https://doi.org/10.1145/3557992.3565993
https://doi.org/10.1145/3557992.3565993
Publikováno v:
Datenbank-Spektrum. 21:213-223
When using arbitrarily oriented subspace clustering algorithms one obtains a partitioning of a given data set and for each partition its individual subspace. Since clustering is an unsupervised machine learning task, we may not have “ground truth
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d69a9220dab426d4a449788b675d5a15
https://doi.org/10.1007/s13222-021-00388-6
https://doi.org/10.1007/s13222-021-00388-6
Publikováno v:
Data Science and Engineering, Vol 5, Iss 4, Pp 360-374 (2020)
OPTICS is a popular tool to analyze the clustering structure of a dataset visually. The created two-dimensional plots indicate very dense areas and cluster candidates in the data as troughs. Each horizontal slice represents an outcome of a density-ba
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ea04d3667b390cfe14df5ead7e368e93
https://doaj.org/article/7c5031f405f446c684602f3c38e3ffb3
https://doaj.org/article/7c5031f405f446c684602f3c38e3ffb3
Publikováno v:
ICDM (Workshops)
Having data with a high number of features raises the need to detect clusters which exhibit within subspaces of features a high similarity. These subspaces can be arbitrarily oriented which gave rise to arbitrarily-oriented subspace clustering (AOSC)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d18368cd9f3c7c82ecaf01712cdb1425
https://doi.org/10.1109/icdmw51313.2020.00050
https://doi.org/10.1109/icdmw51313.2020.00050
Publikováno v:
ICDM (Workshops)
In this work we propose SRE, the first internal evaluation measure for arbitrary oriented subspace clustering results. For this purpose we present a new perspective on the subspace clustering task: the goal we formalize is to compute a clustering whi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::13fee5853e035150a06a97d7e4674bb8
https://doi.org/10.1109/icdmw51313.2020.00051
https://doi.org/10.1109/icdmw51313.2020.00051
Publikováno v:
ICDM (Workshops)
In the setting of unsupervised machine learning, especially in clustering tasks, the evaluation of either novel algorithms or the assessment of a clustering of novel data is challenging. While mostly in the literature the evaluation of new methods is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b4d3b795161fff3ad67df56ef94b2954
https://doi.org/10.1109/icdmw51313.2020.00049
https://doi.org/10.1109/icdmw51313.2020.00049
Publikováno v:
Advances in Knowledge Discovery and Data Mining ISBN: 9783030474256
PAKDD (1)
PAKDD (1)
When facing high-dimensional data streams, clustering algorithms quickly reach the boundaries of their usefulness as most of these methods are not designed to deal with the curse of dimensionality. Due to inherent sparsity in high-dimensional data, d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8ee8172fa0a1cda4465c49b341d0e389
https://doi.org/10.1007/978-3-030-47426-3_28
https://doi.org/10.1007/978-3-030-47426-3_28
Publikováno v:
Database Systems for Advanced Applications ISBN: 9783030594091
DASFAA (1)
DASFAA (1)
OPTICS is a popular tool to analyze the clustering structure of a dataset visually. The created two-dimensional plots indicate very dense areas and cluster candidates in the data as troughs. Each horizontal slice represents an outcome of a density-ba
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a150baed277e8745b433318cf154d367
https://doi.org/10.1007/978-3-030-59410-7_52
https://doi.org/10.1007/978-3-030-59410-7_52