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/*!
@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2020
@copyright BSD-3-Clause
*/
#pragma once
#include <pyclustering/cluster/bsas.hpp>
#include <pyclustering/cluster/mbsas_data.hpp>
#include <pyclustering/utils/metric.hpp>
using namespace pyclustering::utils::metric;
namespace pyclustering {
namespace clst {
/*!
@class mbsas mbsas.hpp pyclustering/cluster/mbsas.hpp
@brief Class represents MBSAS (Modified Basic Sequential Algorithmic Scheme).
@details Interface of MBSAS algorithm is the same as for BSAS. This algorithm performs clustering in two steps.
The first - is determination of amount of clusters. The second - is assignment of points that were not
marked as a cluster representatives to clusters.
*/
class mbsas : public bsas {
public:
/*!
@brief Default constructor of the clustering algorithm.
*/
mbsas() = default;
/*!
@brief Creates MBSAS algorithm using specified parameters.
@param[in] p_amount: amount of clusters that should be allocated.
@param[in] p_threshold: threshold of dissimilarity (maximum distance) between points.
@param[in] p_metric: metric for distance calculation between points.
*/
mbsas(const std::size_t p_amount,
const double p_threshold,
const distance_metric<point> & p_metric = distance_metric_factory<point>::euclidean());
public:
/*!
@brief Performs cluster analysis of an input data.
@param[in] p_data: input data for cluster analysis.
@param[out] p_result: clustering result of an input data.
*/
void process(const dataset & p_data, mbsas_data & p_result);
};
}
}
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