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# PreDeCon - Projected Clustering
# Paper: Density Connected Clustering with Local Subspace Preferences, Böhm et al., 2004
# This code is not optimized.
import numpy as np
def get_neighbor(X, candidate):
"""Return the eps-neighbors of candidate.
"""
neighbors = []
for pt in X:
if ((pt - candidate) ** 2).sum() ** .5 <= eps:
neighbors.append(pt)
return neighbors
def get_weights(X, candidate):
dists_x = []
dists_y = []
for neighbor in get_neighbor(X, candidate):
dists_x.append((neighbor[0] - candidate[0]) ** 2)
dists_y.append((neighbor[1] - candidate[1]) ** 2)
var_x = sum(dists_x) / len(dists_x)
var_y = sum(dists_y) / len(dists_y)
weight_x = 1 if var_x > delta else K
weight_y = 1 if var_y > delta else K
return weight_x, weight_y
def pref_weighted_dist(X, neighbor, candidate):
weights = get_weights(X, neighbor)
dist = 0
for i in range(2):
dist += weights[i] * (neighbor[i] - candidate[i]) ** 2
return dist ** .5
def is_core(X, candidate):
good_ones = []
for neighbor in get_neighbor(X, candidate):
dist = max(
pref_weighted_dist(X, neighbor, candidate),
pref_weighted_dist(X, candidate, neighbor)
)
if dist <= eps:
good_ones.append(dist)
return len(good_ones) >= minpts
X = np.array([
[1, 5],
[2, 5],
[3, 5], # p3
[4, 5],
[5, 5],
[6, 5], # p6
[7, 5],
[7, 6],
[7, 7],
[7, 4],
[7, 3],
[7, 2]
])
minpts = 3
eps = 1
delta = 0.25
K = 100
print('p3', is_core(X, X[2]))
print('p5', is_core(X, X[5]))
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@instance01 instance01 commented Aug 2, 2020

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