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.
The contribution is the result of joint reflections by the authors, with the following contributions attributed to S. L’Abbate (chapter 2, 3, 4) and to P. Perchinunno (chapter 1, 5).
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References
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland (1996)
Ester, M., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd ACM SIGKDD, Portland, OR, USA, pp. 226–231 (1996)
Montrone, S., et al.: A modified density-based algorithm for clustering. In: Supplemento ai Rendiconti del Circolo Matematico di Palermo. II 84, Palermo, Italy, pp. 231–239 (2012)
Montrone, S., et al.: Comparing SaTScan and DBSCAN methods inspatial phenomena. In: SPATIAL2, Spatial Data Methods for Environmental and Ecological Processes, 2nd edn. Baia delle Zagare, Foggia, Italy, pp. 116–119 (2011)
Montrone, S., et al.: Statistical methods for detecting geographical clustering of housing poverty. In: Rivista Italiana di Economia, Demografia e Statistica, vol. LXV. 3/4. SIEDS, Roma, Italy, pp. 157–164 (2011)
Metz, C.E.: Basic principles of ROC analysis. Seminars Nucl. Med. VIII(4), 283–298 (1978)
Metz, C.E.: ROC Methodology in radiologic imaging. Invest. Radiol. 21, 720–733 (1986)
Pepe, M.S.: The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press, New York (2003)
Zou, K.H.: Receiver operating characteristic (ROC) literature research (2002). http://splweb.bwh.harvard.edu:8000/pages/ppl/zou/roc.html
Hanley, J.A., McNeil, B.I.: A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148(3), 839–843 (1983)
Hanley, J.A., McNeil, B.I.: Statistical approaches to the analysis of receiver operating characteristic (ROC) curves. Med. Decis. Making 4, 137–150 (1984)
Montrone, S., Massari, A., Perchinunno, P., Ligorio, C., L'Abbate, S.: Statistical Methods for Detecting Geographical Clustering of Housing poverty. In: Rivista Italiana di Economia, Demografia e Statistica, vol. LXV, no. 3/4 (2011). ISSN: 0035-6832
Efron, B.: Bootstrap methods: another look at the jackknife. In: The Annals of Statistics, vol. 7, pp. 1–26. Institute of Mathematical Statistics (1979)
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Perchinunno, P., L‘Abbate, S. (2022). A New Approach on Density-Based Algorithm for Clustering Dense Areas. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds) Computational Science and Its Applications – ICCSA 2022 Workshops. ICCSA 2022. Lecture Notes in Computer Science, vol 13377. Springer, Cham. https://doi.org/10.1007/978-3-031-10536-4_35
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