Python——K-means聚类分析及其结果可视化聚类分析及其结果可视化
0 前言前言
K-Means是聚类算法的一种，通过距离来判断数据点间的相似度并据此对数据进行聚类。
1 聚类算法聚类算法
科学计算中的聚类方法
方法名称方法名称 参数参数 可伸缩性可伸缩性 用例用例
几何形状几何形状
（使用的指（使用的指
标）标）
K-Means
number of
clusters
Very large , medium
with MiniBatch
coden_samplesn_clusters
General-
purpose,
even
cluster size,
flat
geometry,
not too
many
clusters
Distances
between
points
Affinity
propagation
damping,
sample
preference
Not scalable with
n_samples
Many
clusters,
uneven
cluster size,
non-flat
geometry
Graph
distance
(e.g.
nearest-
neighbor
graph)
Mean-shift bandwidth
Not scalable with
n_samples
Many
clusters,
uneven
cluster size,
non-flat
geometry
Distances
between
points
Spectral
clustering
number of
clusters
Medium , small
n_samplesn_clusters
Few
clusters,
even
cluster size,
non-flat
geometry
Graph
distance
(e.g.
nearest-
neighbor
graph)
Ward
hierarchical
clustering
number of
clusters or
distance
threshold
Large and
n_samplesn_clusters
Many
clusters,
possibly
connectivity
constraints
Distances
between
points
Agglomerative
clustering
number of
clusters or
distance
threshold,
linkage type,
distance
Large and
n_samplesn_clusters
Many
clusters,
possibly
connectivity
constraints,
non
Euclidean
distances
Any pairwise
distance
DBSCAN
neighborhood
size
Very large , medium
n_samplesn_clusters
Non-flat
geometry,
uneven
cluster
sizes
Distances
between
nearest
points
OPTICS
minimum
cluster
membership
Very large , large
n_samplesn_clusters
Non-flat
geometry,
uneven
cluster
sizes,
variable
cluster
density
Distances
between
points
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