This paper presents a new approach to density-based clustering for the identification of dense areas. In particular, the focus is on identification of breast masses in the X-ray imaging of a mammography. The idea was to apply cluster analysis by identifying breast masses as clusters, understood as dense regions of space separated by areas of lower density. Attention was focused on a particular method of clustering based on density, the DBSCAN, proposing a new approach by applying it to a real dataset: a supervised approach, based on ROC curves and a weighted distance, for the choice of input parameters.
A New Approach on Density-Based Algorithm for Clustering Dense Areas
labbate samuela;perchinunno paola
2022-01-01
Abstract
This paper presents a new approach to density-based clustering for the identification of dense areas. In particular, the focus is on identification of breast masses in the X-ray imaging of a mammography. The idea was to apply cluster analysis by identifying breast masses as clusters, understood as dense regions of space separated by areas of lower density. Attention was focused on a particular method of clustering based on density, the DBSCAN, proposing a new approach by applying it to a real dataset: a supervised approach, based on ROC curves and a weighted distance, for the choice of input parameters.File in questo prodotto:
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