docs: update docs for mst and union find

README.md

@@ -107,9 +107,10 @@ Gradle is used for development.
       * Post-order DFS
       * Kahn's
     * Floyd Warshall 
-10. Minimum spanning tree (**WIP**)
-    * Prim's
-    * Kruskal's
+10. [Minimum spanning tree](src/main/java/algorithms/minimumSpanningTree)
+    * [Prim](src/main/java/algorithms/minimumSpanningTree/prim)
+    * [Kruskal](src/main/java/algorithms/minimumSpanningTree/kruskal)
+    * Boruvska (**WIP**)
 
 ## Set-up 
 If you are a CS2040s student, your IDEA configurations should already be compatible with this project structure. So, 

src/main/java/dataStructures/disjointSet/README.md

@@ -3,8 +3,8 @@
 ## Background
 
 A disjoint-set structure also known as a union-find or merge-find set, is a data structure that tracks a set of elements
-partitioned into a number of disjoint (non-overlapping) subsets. It is also commonly used to check for connectivity 
-(e.g. if two objects are 'grouped' together or belong to some component).
+partitioned into a number of disjoint (non-overlapping) subsets. It is commonly used to check for connectivity 
+(e.g. if two objects are 'grouped' together/belong to some component).
 
 In CS2040s, this is introduced in the context of checking for dynamic connectivity. For instance, Kruskal's algorithm
 in graph theory to find minimum spanning tree of a graph utilizes disjoint set to efficiently

src/main/java/dataStructures/disjointSet/weightedUnion/README.md

@@ -9,6 +9,14 @@ The root node of each tree is used as the identity for all elements in the same
 Note that the trees here are not necessarily binary trees. In fact, more often than not, we will have nodes
 with multiple children nodes.
 
+<div align="center">
+    <img src="../../../../../../docs/assets/images/QuickUnion1.png" width="40%">
+    LEADS TO ->
+    <img src="../../../../../../docs/assets/images/QuickUnion2.png" width="40%">
+    <br>
+    Credits: CS2040s Lecture Slides
+</div>
+
 ### Union
 Between the two components, decide on the component to represent the combined set as before.
 Now, union is simply assigning the root node of one tree to be the child of the root node of another. Hence, its name.
@@ -72,6 +80,15 @@ the traversal of a node up to the root, we re-assign each node's parent to be th
 assigning to its grandparent actually suffice and yield the same big-O upper-bound! This allows path compression to be
 done in a single pass.). By doing so, we greatly reduce the height of the trees formed.
 
+_Note: Below shows the 2-pass version of path compression, but what's implemented is the 1-pass version of assigning to 
+grandparents._
+
+<div align="center">
+    <img src="../../../../../../docs/assets/images/PathCompression.png" width="50%">
+    <br>
+    Credits: CS2040s Lecture Slides
+</div>
+
 The analysis with compression is a bit trickier here and talks about the inverse-Ackermann function. 
 Interested readers can find out more [here](https://dl.acm.org/doi/pdf/10.1145/321879.321884).