![](https://csdnimg.cn/release/download_crawler_static/8828447/bg1.jpg)
Density-Based Outlier Detection
• Local outliers: Outliers comparing to their local
neighborhoods, instead of the global data
distribution
• In Fig., o
1
and o2 are local outliers to C
1
, o
3
is a global
outlier, but o
4
is not an outlier. However, proximity-
based clustering cannot find o
1
and o
2
are outlier
(e.g., comparing with O
4
).
1
Intuition (density-based outlier detection): The density around an outlier
object is significantly different from the density around its neighbors
Method: Use the relative density of an object against its neighbors as
the indicator of the degree of the object being outliers
k-distance of an object o, dist
k
(o): distance between o and its k-th NN
k-distance neighborhood of o, N
k
(o) = {o’| o’ in D, dist(o, o’) ≤ dist
k
(o)}
N
k
(o) could be bigger than k since multiple objects may have
identical distance to o