incremental_dbscan_description.txt

+++++++++++++++++++++++ Incremental DBSCAN Overall Architecture ++++++++++++++++++++++++++++++++
+                                                                                              +
+    After the initial train of the DBSCAN is complete, the Incremental DBSCAN takes place.    +
+    The pseudo-code for the Incremental DBSCAN algorithm is presented below.                  +
+    Its job is to predict where the incoming data are clustered, and update the algorithm.    +
+                                                                                              +
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+++++++++++++++++++++++++++++ Incremental DBSCAN Algorithm +++++++++++++++++++++++++++++++++++++
+                                                                                              +
+                     Pseudo-code of the Incremental DBSCAN Algorithm                          +
+                                                                                              +
+                                                                                              +
+                                                                                              +
+    1. Parameters used                                                                        +
+        1.1. D <-- A dataset containing n objects {X1, X2, ... , Xn}                          +
+                                                                                              +
+        1.2. eps <-- radius of the cluster                                                    +
+                                                                                              +
+        1.3. minPts <-- minimum number of data objects (minimum minPts should be              +
+                        (Dimensions of the dataset + 1))                                      +
+                                                                                              +
+        1.4. Ci (where i=1, 2, 3, ..., m) <-- new data item                                   +
+                                                                                              +
+                                                                                              +
+    2. K <-- Already existing clusters                                                        +
+                                                                                              +
+                                                                                              +
+    3. New data (streams) arrive to the engine. They will be clustered directly, based on     +
+       the minimum mean (M) between the data and the core objects of the already existing     +
+       clusters.                                                                              +
+                                                                                              +
+                                                                                              +
+    4. FOR (i = 1:m):                                                                         +
+        4.1. Find mean dis(Ci, M)distance between the item and the core elements of each      +
+             cluster.                                                                         +
+                                                                                              +
+             NOTE: A point is a CORE POINT if at least minPts are within distance eps of      +
+             it. In other words, a point is a CORE POINT if it is part of a cluster and       +
+             not an outlier.                                                                  +
+                                                                                              +
+        4.2. IF ((dis(Ci, M) is minimum) AND (Ci <= eps) AND (size(Kp) >= minPts)):           +
+              4.2.1. Unite cluster Kp with the item Ci (Kp U Ci)                              +
+                                                                                              +
+        4.3. ELSE IF ((dis(Ci, M) != minimum) OR (Ci > eps) OR (size(Kp)<minPts)):            +
+              4.3.1. Set item as outlier (Oi)                                                 +
+                                                                                              +
+        4.4. ELSE IF (Count(Oi)>minPts):                                                      +
+              4.4.1. Form new cluster with the Oi                                             +
+                                                                                              +
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+++++++++++++++++++++++++++++++++++++ Taken from +++++++++++++++++++++++++++++++++++++++++++++++
+                                                                                              +
+                   Chakraborty, S., & Nagwani, N. K. (2014). Analysis and                     +
+                   study of Incremental DBSCAN clustering algorithm. arXiv                    +
+                   preprint arXiv:1406.4754.                                                  +
+                                                                                              +
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