Skip to main content
Data Science

Return to Answer

added 11 characters in body
Source Link
Brian Spiering
Brian Spiering
  • 22.3k
  • 2
  • 28
  • 113

Each of those selectedSeveral scikit-learn clustering algorithms can be fit using cosine distances in scikit-learn:

from sklearn.clustercollections import DBSCAN, MeanShift, OPTICS  import defaultdict
from sklearn.metricsdatasets import load_iris
from sklearn.pairwisecluster  import cosine_distancesDBSCAN, OPTICS

# Define clusteringsample algorithmsdata
algorithmsiris = [DBSCAN,load_iris()
X MeanShift,= OPTICS]iris.data

# PlaceholderList forclustering resultsalgorithms
resultsalgorithms = dict.fromkeys((a.__name__[DBSCAN, forOPTICS] a# inMeanShift algorithms))does not use a metric

# Fit each clustering algorithm and store results
results = defaultdict(int)
for algorithm in algorithms:
    results[algorithm] = algorithm(metric=cosine_distancesmetric='cosine').fit(X)

Each of those selected clustering algorithms can be fit using cosine distances in scikit-learn:

from sklearn.cluster import DBSCAN, MeanShift, OPTICS
from sklearn.metrics.pairwise import cosine_distances

# Define clustering algorithms
algorithms = [DBSCAN, MeanShift, OPTICS]

# Placeholder for results
results = dict.fromkeys((a.__name__ for a in algorithms))

# Fit each clustering algorithm and store results
for algorithm in algorithms:
    results[algorithm] = algorithm(metric=cosine_distances).fit(X)

Several scikit-learn clustering algorithms can be fit using cosine distances:

from collections      import defaultdict
from sklearn.datasets import load_iris
from sklearn.cluster  import DBSCAN, OPTICS

# Define sample data
iris = load_iris()
X = iris.data

# List clustering algorithms
algorithms = [DBSCAN, OPTICS] # MeanShift does not use a metric

# Fit each clustering algorithm and store results
results = defaultdict(int)
for algorithm in algorithms:
    results[algorithm] = algorithm(metric='cosine').fit(X)
deleted 11 characters in body
Source Link
Brian Spiering
Brian Spiering
  • 22.3k
  • 2
  • 28
  • 113

Each of those selected clustering algorithms can be fit using cosine distances in scikit-learn:

from sklearn.cluster import DBSCAN, MeanShift, OPTICS
from sklearn.metrics.pairwise import cosine_distances

# Define clustering algorithms
algorithms = [DBSCAN, MeanShift, OPTICS]

# Placeholder for results
results = dict.fromkeys((a.__name__ for a in algorithms))

# Fit each clustering algorithm and store results
for algorithm in clustering_algorithmsalgorithms:
    results[algorithm] = algorithm(metric=cosine_distances).fit(X)

Each of those selected clustering algorithms can be fit using cosine distances in scikit-learn:

from sklearn.cluster import DBSCAN, MeanShift, OPTICS
from sklearn.metrics.pairwise import cosine_distances

# Define clustering algorithms
algorithms = [DBSCAN, MeanShift, OPTICS]

# Placeholder for results
results = dict.fromkeys((a.__name__ for a in algorithms))

# Fit each clustering algorithm and store results
for algorithm in clustering_algorithms:
    results[algorithm] = algorithm(metric=cosine_distances).fit(X)

Each of those selected clustering algorithms can be fit using cosine distances in scikit-learn:

from sklearn.cluster import DBSCAN, MeanShift, OPTICS
from sklearn.metrics.pairwise import cosine_distances

# Define clustering algorithms
algorithms = [DBSCAN, MeanShift, OPTICS]

# Placeholder for results
results = dict.fromkeys((a.__name__ for a in algorithms))

# Fit each clustering algorithm and store results
for algorithm in algorithms:
    results[algorithm] = algorithm(metric=cosine_distances).fit(X)
deleted 10 characters in body
Source Link
Brian Spiering
Brian Spiering
  • 22.3k
  • 2
  • 28
  • 113

Here is how you can fit eachEach of those selected clustering algorithmalgorithms can be fit using cosine distances in scikit-learn:

from sklearn.cluster import DBSCAN, MeanShift, OPTICS
from sklearn.metrics.pairwise import cosine_distances

# Define clustering algorithms
algorithms = [DBSCAN, MeanShift, OPTICS] 

# Placeholder for results
results = dict.fromkeys((a.__name__ for a in algorithms))

# Fit each clustering algorithm and store results
for algorithm in clustering_algorithms:
    results[algorithm] = algorithm(metric=cosine_distances).fit(X)

Here is how you can fit each of those selected clustering algorithm using cosine distances in scikit-learn:

from sklearn.cluster import DBSCAN, MeanShift, OPTICS
from sklearn.metrics.pairwise import cosine_distances

# Define clustering algorithms
algorithms = [DBSCAN, MeanShift, OPTICS]
# Placeholder for results
results = dict.fromkeys((a.__name__ for a in algorithms))

# Fit each clustering algorithm and store results
for algorithm in clustering_algorithms:
    results[algorithm] = algorithm(metric=cosine_distances).fit(X)

Each of those selected clustering algorithms can be fit using cosine distances in scikit-learn:

from sklearn.cluster import DBSCAN, MeanShift, OPTICS
from sklearn.metrics.pairwise import cosine_distances

# Define clustering algorithms
algorithms = [DBSCAN, MeanShift, OPTICS] 

# Placeholder for results
results = dict.fromkeys((a.__name__ for a in algorithms))

# Fit each clustering algorithm and store results
for algorithm in clustering_algorithms:
    results[algorithm] = algorithm(metric=cosine_distances).fit(X)
Source Link
Brian Spiering
Brian Spiering
  • 22.3k
  • 2
  • 28
  • 113