@inproceedings{3168086,
    title = "Detecting hyperplane clusters with adaptive possibilistic clustering",
    author = "Koutroumbas, K. D. and Xenaki, S. D. and Rontogiannis, A. A.",
    year = "2016",
    publisher = "ASSOCIATION FOR COMPUTING MACHINERY",
    booktitle = "9TH HELLENIC CONFERENCE ON ARTIFICIAL INTELLIGENCE (SETN 2016)",
    doi = "10.1145/2903220.2903236",
    keywords = "possibilistic clustering; adaptivity; hyperplane clusters",
    abstract = "In this paper the problem of detecting clusters whose points are spread
along an (1 1)-dimensional hyperplane in an 1-dimensional space is
considered. More specifically, the recently proposed adaptive
possibilistic c-means algorithm is modified in order to cope with this
type of clusters. The main advantage of the proposed method is that it
does not require a priori knowledge of the exact number of clusters.
Instead, it begins with an overestimated number of them and
(potentially) ends up with the true number of them. Preliminary results
of the proposed algorithm on both synthetic and real data verify its
validity."
}