Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Emin Aksehirli"'
Autor:
Zhe Chen, Emin Aksehirli
Publikováno v:
WCNC
Optimizing capital expenditure (CapEx) has been an increasingly important objective in telco operators’ cell planning process. Traditionally, neighbor cell relation is operationally managed and independent from capacity planning. In this paper, we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c52d9a418263e59f432e8c28cdad59f4
https://doi.org/10.1109/wcnc45663.2020.9120809
https://doi.org/10.1109/wcnc45663.2020.9120809
Autor:
Emin Aksehirli, Ying Li
Publikováno v:
ICDM Workshops
Singapore has a significantly high coverage of both public transportation and communication network. Island-wide Mass Rapid Transport (MRT) system is the backbone of public transport, any improvement or disruption to its service can create a consider
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b3c1b6b373898a763aedff88f03df984
https://doi.org/10.1109/icdmw.2018.00098
https://doi.org/10.1109/icdmw.2018.00098
Autor:
Selim Mimaroglu, Emin Aksehirli
Publikováno v:
IEEE/ACM Transactions on Computational Biology and Bioinformatics. 9:408-420
Clustering has a long and rich history in a variety of scientific fields. Finding natural groupings of a data set is a hard task as attested by hundreds of clustering algorithms in the literature. Each clustering technique makes some assumptions abou
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ed434c021f7fa86bae7cf99202c52d76
https://doi.org/10.1109/tcbb.2011.129
https://doi.org/10.1109/tcbb.2011.129
Autor:
Selim Mimaroglu, Emin Aksehirli
Publikováno v:
Pattern Recognition Letters. 32:1572-1580
Clustering is the process of assigning a set of physical or abstract objects into previously unknown groups. The goal of clustering is to group similar objects into the same clusters and dissimilar objects into different clusters. Similarities betwee
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9962208323a079082cdcf38557aead1e
https://doi.org/10.1016/j.patrec.2011.06.003
https://doi.org/10.1016/j.patrec.2011.06.003
Publikováno v:
ICDM Workshops
IEEE 15th International Conference on Data Mining Workshops (ICDMW), NOV 14-17, 2015, ATlantic city, NJ
2015 IEEE International Conference on Data Mining Workshop (ICDMW)
IEEE 15th International Conference on Data Mining Workshops (ICDMW), NOV 14-17, 2015, ATlantic city, NJ
2015 IEEE International Conference on Data Mining Workshop (ICDMW)
Clustering high dimensional datasets is challenging due to the curse of dimensionality. One approach to address this challenge is to search for subspace clusters, i.e., clusters present in subsets of attributes. Recently the cartification algorithm w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6454085cf9076441d74711cbce2da1ab
https://lirias.kuleuven.be/handle/123456789/529073
https://lirias.kuleuven.be/handle/123456789/529073
Publikováno v:
Lecture notes in computer science
Big Data Analytics and Knowledge Discovery ISBN: 9783319227283
DaWaK
Big Data Analytics and Knowledge Discovery ISBN: 9783319227283
DaWaK
Detecting cluster structures seems to be a simple task, i.e. separating similar from dissimilar objects. However, given today's complex data, (dis-)similarity measures and traditional clustering algorithms are not reliable in separating clusters from
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2d8fbbe796c11f8e02bf292b97e79061
https://hdl.handle.net/10067/1294990151162165141
https://hdl.handle.net/10067/1294990151162165141
Publikováno v:
ICDM
Data Mining (ICDM) : 2013 IEEE 13th International Conference on Data Mining, 7-10 December 2013, Dallas, Texas, USA
Data Mining (ICDM) : 2013 IEEE 13th International Conference on Data Mining, 7-10 December 2013, Dallas, Texas, USA
The analysis of high dimensional data comes with many intrinsic challenges. In particular, cluster structures become increasingly hard to detect when the data includes dimensions irrelevant to the individual clusters. With increasing dimensionality,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::26d863cac6d3c175e243a967e0dd6a8a
https://doi.org/10.1109/icdm.2013.146
https://doi.org/10.1109/icdm.2013.146
Publikováno v:
BigData Conference
IEEE Big Data 2013 : International Conference on Big Data, October 6-9, 2013, Santa Clara, Calif., USA
IEEE Big Data 2013 : International Conference on Big Data, October 6-9, 2013, Santa Clara, Calif., USA
Frequent Itemset Mining (FIM) is one of the most well known techniques to extract knowledge from data. The combinatorial explosion of FIM methods become even more problematic when they are applied to Big Data. Fortunately, recent improvements in the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a4da20005cb8aa8ce30c7d1bf301d5f4
https://doi.org/10.1109/bigdata.2013.6691742
https://doi.org/10.1109/bigdata.2013.6691742