TY - GEN
T1 - Semi-supervised clustering method for multi-density data
AU - Atwa, Walid
AU - Li, Kan
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Finding clusters is a challenging problem especially when the clusters are being of widely varied shapes, sizes, and densities. Density-based clustering methods are the most important due to their high ability to detect arbitrary shaped clusters. However, they are depending on two specified parameters (Eps and Minpts) that define a single density. Moreover, most of these methods are unsupervised, which cannot improve the clustering quality by utilizing a small number of prior knowledge. In this paper we show how background knowledge can be used to bias a density-based clustering method for multi-density data. Experimental results confirm that the proposed method gives better results than other semi-supervised and unsupervised clustering algorithms.
AB - Finding clusters is a challenging problem especially when the clusters are being of widely varied shapes, sizes, and densities. Density-based clustering methods are the most important due to their high ability to detect arbitrary shaped clusters. However, they are depending on two specified parameters (Eps and Minpts) that define a single density. Moreover, most of these methods are unsupervised, which cannot improve the clustering quality by utilizing a small number of prior knowledge. In this paper we show how background knowledge can be used to bias a density-based clustering method for multi-density data. Experimental results confirm that the proposed method gives better results than other semi-supervised and unsupervised clustering algorithms.
UR - http://www.scopus.com/inward/record.url?scp=84949961849&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-22324-7_33
DO - 10.1007/978-3-319-22324-7_33
M3 - Conference contribution
AN - SCOPUS:84949961849
SN - 9783319223230
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 313
EP - 319
BT - Database Systems for Advanced Applications - DASFAA 2015 International Workshops, SeCoP, BDMS, and Posters, Revised Selected Papers
A2 - Ishikawa, Yoshiharu
A2 - Nutanong, Sarana
A2 - Liu, An
A2 - Qian, Tieyun
A2 - Cheema, Muhammad Aamir
PB - Springer Verlag
T2 - 2nd International Workshop on Semantic Computing and Personalization, SeCoP 2015, 2nd International Workshop on Big Data Management and Service, BDMS 2015 held in conjunction with 20th International Conference on Database Systems for Advanced Applications, DASFAA 2015
Y2 - 20 April 2015 through 23 April 2015
ER -