def process(self):
"""!
@brief Performs cluster analysis in line with rules of ROCK algorithm.
@return (rock) Returns itself (ROCK instance).
@see get_clusters()
"""
# TODO: (Not related to specification, just idea) First iteration should be investigated. Euclidean distance should be used for clustering between two
# points and rock algorithm between clusters because we consider non-categorical samples. But it is required more investigations.
if self.__ccore is True:
self.__clusters = wrapper.rock(self.__pointer_data, self.__eps,
self.__number_clusters,
self.__threshold)
else:
self.__clusters = [[index]
for index in range(len(self.__pointer_data))]
while len(self.__clusters) > self.__number_clusters:
indexes = self.__find_pair_clusters(self.__clusters)
if indexes != [-1, -1]:
self.__clusters[indexes[0]] += self.__clusters[indexes[1]]
self.__clusters.pop(indexes[1]) # remove merged cluster.
else:
break # totally separated clusters have been allocated
return self
def process(self):
"""!
@brief Performs cluster analysis in line with rules of ROCK algorithm.
@remark Results of clustering can be obtained using corresponding get methods.
@see get_clusters()
"""
# TODO: (Not related to specification, just idea) First iteration should be investigated. Euclidean distance should be used for clustering between two
# points and rock algorithm between clusters because we consider non-categorical samples. But it is required more investigations.
if (self.__ccore is True):
self.__clusters = wrapper.rock(self.__pointer_data, self.__eps,
self.__number_clusters,
self.__threshold)
else:
self.__clusters = [[index]
for index in range(len(self.__pointer_data))]
while (len(self.__clusters) > self.__number_clusters):
indexes = self.__find_pair_clusters(self.__clusters)
if (indexes != [-1, -1]):
self.__clusters[indexes[0]] += self.__clusters[indexes[1]]
self.__clusters.pop(indexes[1])
# remove merged cluster.
else:
break
def process(self):
if (self.__ccore is True):
self.__clusters = wrapper.rock(self.__pointer_data, self.__eps,
self.__number_clusters,
self.__threshold)
else:
self.__clusters = [[index]
for index in range(len(self.__pointer_data))]
while (len(self.__clusters) > self.__number_clusters):
indexes = self.__find_pair_clusters(self.__clusters)
if (indexes != [-1, -1]):
self.__clusters[indexes[0]] += self.__clusters[indexes[1]]
self.__clusters.pop(indexes[1])
# remove merged cluster.
else:
break