Update README.md

README.md

@@ -1,24 +1,54 @@
 # <p align="center"> RangeSearchAnalysis </p>
 ## <p align="center"> Algorithm of Analysis individual project #1 - Range searching algorithm </p>
 
+## Main classes that runds individually.
+ * Main.java
+      * Takes integer input based on the input files. If input files are in three dimension, user has to put, for example, [5 10 25] separated by the space(exclude brackets). 
+      * Executes sequential construction and KD-KNN-tree construction based on the input files in data with specified file name.
+      * If data file for input is empty, CoordinateGen.java execution is required before running Main.java program in order to make arbitrary input files.
+
+ * CoordinateGen.java
+
+## This project is comprised of three major sections.
 
-This project is comprised of three major sections.
+For every distance calcuation, Euclidean distance is used.
 
-### 1. Point Generator.
-  * Generates arbiturary points in incremental order.
-      * Matrix dimesion determined by users input.
-      * Matrix starting from 100 * 2 ^ n, where n = 1, 2, 3, 4... (Each of the matrix length is identical)
-      * Experimental number of points generated 600 * n, where n = 1, 2, 3, 4...
-      * Each generated points are save in .dat file named like: "POINTS_*MATRIX LENGTH*_*NUMBER OF POINTS*.dat
+### 1. Point Generator(**CoordinateGen.java**).
+  * Generates arbiturary points in random order.
+      * Matrix dimesion determined by getting users input.
+      * Matrix point range and number of points grow larger.
+      * When generated input file name is BIG_DATA_CASE10_20, this means the generated random points have maximum point range from 1 to 10 with number of points of 20. 
 
 ### 2. Data structures.
-  * Sequential structure.
-    * insert()
-    * find()
-
-  * KD-Tree Structure.
-    * insert()
-    * find()
+  * Linked List (**SequentialAlgorithm.java**).
+    * insert(list)
+      > Append additional coordinate points to the end
+    * findClosestDistance(targetPoints)
+      > Visit every node to verify the shortest distance   
+ 
+  * KD-Tree (**KDTreeIntegers.java**).
+    * Class.AxisSort
+      > comparator that sorts an ArrayList by axis index
+    * insert(depth, listOfPoints, currentNode, parentNode)
+      > Builds a binaray-tree like data structure.
+    * findKDTreeNormal(targetPoints)
+      > finds a approximately close distance to the targetPoints
+  
+  * KNN-Search (method in **KDTreeIntegers.java**).
+    * findKNNAlgorithm(targetPoints, node)
+      > find closest distance from the targetPoints with the result node from KD-Tree search
 
 ### 3. Result exportation.
+   * All summary data are exported into .cell file. Data contains:
+      > General cell
+      >> Numeric value range.
+      >> Number of points.
+      >> Desired coordinationto look up.
+
+      > Each cell contains
+      >> Closest coordinate points found
+      >> Distance value in double
+      >> Construction time consumption in nano seconds
+      >> Search time consumption in nano seconds
 
+    Time is measured by using ProcessTimeRecorder(System.nanoTime).