#381: Create skeleton of CLIQUE algorithm.

docs/citation.bib

@@ -579,6 +579,20 @@ @article{article::cluster::silhouette::1
     url             = {http://dx.doi.org/10.1016/0377-0427(87)90125-7},
     doi             = {10.1016/0377-0427(87)90125-7},
     publisher       = {Elsevier Science Publishers B. V.},
-} 
+}
 
 
+@article{article::clique::1,
+    author          = "Agrawal, Rakeshand Gehrke, Johannes and Gunopulos, Dimitrios and Raghavan, Prabhakar",
+    title           = "Automatic Subspace Clustering of High Dimensional Data",
+    journal         = "Data Mining and Knowledge Discovery",
+    year            = "2005",
+    month           = "Jul",
+    day             = "01",
+    volume          = "11",
+    number          = "1",
+    pages           = "5--33",
+    issn            = "1573-756X",
+    doi             = "10.1007/s10618-005-1396-1",
+    url             = "https://doi.org/10.1007/s10618-005-1396-1"
+}

pyclustering/cluster/clique.py

@@ -0,0 +1,65 @@
+"""!
+
+@brief Cluster analysis algorithm: CLIQUE
+@details Implementation based on paper @cite article::clique::1.
+
+@authors Andrei Novikov ([email protected])
+@date 2014-2019
+@copyright GNU Public License
+
+@cond GNU_PUBLIC_LICENSE
+    PyClustering is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    PyClustering is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+@endcond
+
+"""
+
+
+class spatial_block:
+    def __init__(self):
+        pass
+
+
+class clique:
+    def __init__(self, data, amount_intervals, density_threshold):
+        self.__data = data
+        self.__amount_intervals = amount_intervals
+        self.__density_threshold = density_threshold
+
+        self.__clusters = []
+        self.__noise = []
+
+        self.__validate_arguments()
+
+
+    def process(self):
+        pass
+
+
+    def get_clusters(self):
+        return self.__clusters
+
+
+    def get_noise(self):
+        return self.__noise
+
+
+    def __validate_arguments(self):
+        if len(self.__data) == 0:
+            raise ValueError("Empty input data. Data should contain at least one point.")
+
+        if self.__amount_intervals <= 0:
+            raise ValueError("Incorrect amount of intervals '%d'. Amount of intervals value should be greater than 0." % self.__amount_intervals)
+
+        if self.__density_threshold < 0:
+            raise ValueError("Incorrect density threshold '%f'. Density threshold should not be negative." % self.__density_threshold)