Consider the following 6 2-dimensional points:

a(5,5), b(5,7), c(7,8), d(8,4), e(3,6), f(4,8)

Assume that we need to find the outlier in this tiny data set. Please apply the Manhattan distance to answer the following questions. (Manhattan distance is defined as follows: in a plane with p1 at (x1, y1) and p2 at (x2, y2), it is |x1 - x2| + |y1 - y2|)

• Consider the distance-based model introduced in class, let be 4, and the size threshold be 3, which data point(s) would be identified as outlier(s)?
• Consider the density-based model introduced in class, let k=3, what is the local outlier factor of data point d and c, respectively?