def __init__(self, data, number_clusters, numlocal, maxneighbor):
        """!
        @brief Constructor of clustering algorithm CLARANS.
        @details The higher the value of maxneighbor, the closer is CLARANS to K-Medoids, and the longer is each search of a local minima.
        
        @param[in] data (list): Input data that is presented as list of points (objects), each point should be represented by list or tuple.
        @param[in] number_clusters (uint): amount of clusters that should be allocated.
        @param[in] numlocal (uint): the number of local minima obtained (amount of iterations for solving the problem).
        @param[in] maxneighbor (uint): the maximum number of neighbors examined.
        
        """
        
        self.__pointer_data = data
        self.__numlocal = numlocal
        self.__maxneighbor = maxneighbor
        self.__number_clusters = number_clusters
        
        self.__clusters = []
        self.__current = []
        self.__belong = []
        
        self.__optimal_medoids = []
        self.__optimal_estimation = float('inf')
def __init__ ( self , data , number_clusters , numlocal , maxneighbor ) : self . __pointer_data = data self . __numlocal = numlocal self . __maxneighbor = maxneighbor self . __number_clusters = number_clusters self . __clusters = [ ] self . __current = [ ] self . __belong = [ ] self . __optimal_medoids = [ ] self . __optimal_estimation = float ( 'inf' )
!
        @brief Constructor of clustering algorithm CLARANS.
        @details The higher the value of maxneighbor, the closer is CLARANS to K-Medoids, and the longer is each search of a local minima.
        
        @param[in] data (list): Input data that is presented as list of points (objects), each point should be represented by list or tuple.
        @param[in] number_clusters (uint): amount of clusters that should be allocated.
        @param[in] numlocal (uint): the number of local minima obtained (amount of iterations for solving the problem).
        @param[in] maxneighbor (uint): the maximum number of neighbors examined.