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/*!
@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2020
@copyright BSD-3-Clause
*/
#pragma once
#include <vector>
#include <list>
#include <pyclustering/cluster/cluster_data.hpp>
#include <pyclustering/container/adjacency_matrix.hpp>
#include <pyclustering/definitions.hpp>
using namespace pyclustering::container;
namespace pyclustering {
namespace clst {
using rock_data = cluster_data;
/*!
@class rock rock.hpp pyclustering/cluster/rock.hpp
@brief The class represents a clustering algorithm ROCK.
@details Implementation of the algorithm is based on the paper @cite inproceedings::rock::1.
*/
class rock {
private:
/* for optimization list representation is of clusters is used and than
* it is moved to output result */
using rock_cluster_sequence = std::list<cluster>;
private:
adjacency_matrix m_adjacency_matrix;
double m_radius;
double m_degree_normalization;
size_t m_number_clusters;
rock_cluster_sequence m_clusters;
public:
/**
*
* @brief Default constructor of clustering algorithm.
*
*/
rock();
/**
*
* @brief Creates ROCK solver in line with specified parameters of the algorithm.
*
* @param[in] radius: connectivity radius (similarity threshold), points are neighbors if distance between them is less than connectivity radius.
* @param[in] number_clusters: amount of clusters that should be allocated.
* @param[in] threshold: defines degree of normalization that influences on choice of clusters for merging during processing.
*
*/
rock(const double radius, const std::size_t number_clusters, const double threshold);
/**
*
* @brief Default destructor of the algorithm.
*
*/
~rock() = default;
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, rock_data & p_result);
private:
/**
*
* @brief Creates adjacency matrix where each element described existence of link between points (means that points are neighbors).
*
* @param[in] p_data: input data for cluster analysis.
*
*/
void create_adjacency_matrix(const dataset & p_data);
/**
*
* @brief Finds two clusters that are most suitable candidates for merging and than merges them.
*
*/
bool merge_cluster();
/**
*
* @brief Returns number of link between two clusters.
* @details Link between objects (points) exists only if distance between them less than connectivity radius.
*
* @param[in] cluster1: the first cluster.
* @param[in] cluster2: the second cluster.
*
* @return Number of links between two clusters.
*
*/
size_t calculate_links(const cluster & cluster1, const cluster & cluster2) const;
/**
*
* @brief Calculates coefficient 'goodness measurement' between two clusters.
* @details The coefficient defines level of suitability of clusters for merging.
*
* @param[in] cluster1: the first cluster.
* @param[in] cluster2: the second cluster.
*
* @return Goodness measure between two clusters.
*
*/
double calculate_goodness(const cluster & cluster1, const cluster & cluster2) const;
};
}
}
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