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Parallelized Scaling Algorithm for cluster analysis inpython

knowledge answered on April 14, 2024 Popularity 1/10 Helpfulness 1/10

answer Parallelized Scaling Algorithm for cluster analysis inpython

Parallelized Scaling Algorithm for cluster analysis inpython

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def parallelized_scaling_algorithm(points, k, epsilon):
    # Determine the number of instances based on epsilon
    num_instances = int(1 / epsilon * math.log(1 / epsilon))
    instances = [initialize_instance(i, points, k, epsilon) for i in range(num_instances)]

    best_centers = []
    best_radius = float('inf')

    for instance in instances:
        centers, radius = doubling_algorithm_modified(instance['points'], instance['k'], instance['scale'])
        if radius < best_radius:
            best_radius = radius
            best_centers = centers

    return best_centers, best_radius

def initialize_instance(instance_id, points, k, epsilon):
    # Adjust initialization based on instance_id and epsilon
    scale = calculate_scale(instance_id, epsilon)
    return {'points': points, 'k': k, 'scale': scale}

def doubling_algorithm_modified(points, k, scale):
    # Similar to doubling_algorithm but considers scaling distances
    pass

def calculate_scale(instance_id, epsilon):
    # Calculate the scale for each instance based on its ID and epsilon
    pass

Popularity 1/10 Helpfulness 1/10 Language python

Source: Grepper

Tags: algorithm cluster-analysis python scaling

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Contributed on Apr 14 2024

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