"Simple Application of Clustering Radius  of proximity is specified Two points x 1 and x 2 are ""near"" if d(x 1, x 2 )   Define N(x) – number of poin"
{"Image DocumentName": "Simple Application of Clustering", "Image ImageDescription": "Radius \uf077 of proximity is specified. Two points x1 and x2 are near if d(x1, x2) \uf0a3 \uf077 Define N(x) \u2013 number of points that are within \uf077 of x. Time Complexity O(n2) \uf0de approximation of the algorithm. Fixed-width clustering is first applied. The first point is a center of a cluster. If every subsequent point is near add to a cluster. Otherwise create a new cluster. Approximate N(x) with N(c)\u200f Time Complexity \u2013 O(cn), c - # of clusters. Points in small clusters - anomalies.   E. Eskin et al., A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data, 2002.", "Image XResolution": "37", "Image YResolution": "37", "Image ResolutionUnit": "Pixels/Centimeter", "Image Software": "SlidePlayer", "Image YCbCrPositioning": "Centered"}