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Brian Spiering
Brian Spiering
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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)
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Brian Spiering
Brian Spiering
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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)
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Brian Spiering
Brian Spiering
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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)
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Brian Spiering
Brian Spiering
  • 23.9k
  • 2
  • 30
  • 120