K-means聚类自定义距离计算的开源算法选择

如果你想自己定义一个距离的function的话，scikit-learn是不行的，只支持Euclidean distance

如果你觉得spark可以的话，实际上sprk的k-means也是不行的，好一点的是支持Euclidean distance，还支持cosine distance

如果你想自己定义function处理的话，二个方法：

1、自己实现算法，可参考的文档：

一个简单的讲解

https://medium.com/machine-learning-algorithms-from-scratch/k-means-clustering-from-scratch-in-python-1675d38eee42

一个简单的代码：

https://github.com/pavankalyan1997/Machine-learning-without-any-libraries/tree/master/2.Clustering/1.K_Means_Clustering

这个代码仓库有很多实现不依赖于第三方库

stackoverflow的高赞的回答也实现了一个简单的

https://stackoverflow.com/questions/5529625/is-it-possible-to-specify-your-own-distance-function-using-scikit-learn-k-means

自己实现算法的问题：即使不考虑扩展性，也需要考虑实现多线程版本，还需要看训练的精度

2、找其他开源的项目

参考： https://stackoverflow.com/questions/5529625/is-it-possible-to-specify-your-own-distance-function-using-scikit-learn-k-means

可以引用的nltk的库：

from nltk.cluster.kmeans import KMeansClusterer
NUM_CLUSTERS = <choose a value>
data = <sparse matrix that you would normally give to scikit>.toarray()

kclusterer = KMeansClusterer(NUM_CLUSTERS, distance=nltk.cluster.util.cosine_distance, repeats=25)
assigned_clusters = kclusterer.cluster(data, assign_clusters=True)

可以引用pyclustering 的库

from pyclustering.cluster.kmeans import kmeans
from pyclustering.utils.metric import type_metric, distance_metric

user_function = lambda point1, point2: point1[0] + point2[0] + 2
metric = distance_metric(type_metric.USER_DEFINED, func=user_function)

# create K-Means algorithm with specific distance metric
start_centers = [[4.7, 5.9], [5.7, 6.5]];
kmeans_instance = kmeans(sample, start_centers, metric=metric)

# run cluster analysis and obtain results
kmeans_instance.process()
clusters = kmeans_instance.get_clusters()

对应github地址：https://github.com/annoviko/pyclustering

可以引用tslearn 

if you're clustering time series, you can use the tslearn python package, when you can specify a metric (dtw, softdtw, euclidean).