如何在Python中将dict的值聚类？

Question

Basically, I have a dict in Python with string keys and arrays of ints as values.

dict = {"Option1Results" : [4, 1, 5, 2, 4],
        "Option2Results" : [11, 44, 2, 1, 5],
        ....
        }

I would like to implement hierarchical clustering on this dict based on the intersection of the values. For example, let's say Option1Results and Option4Results share about 70% of the same integers, then cluster them together. Is there a way to go about this other than looping through the dictionary and comparing the values one by one?

Answer 1

I think you could utilize two functions cosine similarity and kmeans

cosine similarity:

Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them.
https://en.wikipedia.org/wiki/Cosine_similarity

data = {'Option{}Results'.format(i):[ random.randint(1,100) for _ in range(5)] for i in range(100)}
pairwise.cosine_similarity(data.values()[0],data.values()[1])
array([[ 0.85988428]])

kmeans:

k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. https://en.wikipedia.org/wiki/K-means_clustering

from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=5, random_state=0).fit(data.values())
kmeans.predict(data['Option70Results'])
array([2])

Answer 2

To find the intersection of the values of the given dict as a set:

intersection = set.intersection(*map(set, dict.values())

Hierarchical clustering can be achieved using scipy's linkage and fcluster. Hierarchical clustering using scipy is explained by this answer .