Add README for HashSet with open-addressing

Author: euchangxian

src/dataStructures/hashSet/openAddressing/README.md

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+# HashSet (Open-addressing)
+Open-addressing is another approach to resolving collisions in hash tables.
+
+A hash collision is resolved by <b>probing<b>, or searching through alternative locations in 
+the array (the probe sequence) until either the target element is found, or an unused array slot is found,
+which indicates that there is no such key in the table.
+
+## Probing Strategies
+
+### Linear Probing
+The probing strategy used in our implementation.
+
+Simplest form of probing and involves linearly searching the hash table for an empty spot upon collision.
+
+However, this method of probing can result in a phenomenon called (primary) clustering where a large run of
+occupied slots builds up, which can drastically degrade the performance of add, remove and contains operations.
+
+h(k, i) = (h'(k) + i) mod m where h'(k) is an ordinary hash function
+
+### Quadratic Probing
+This method of probing involves taking the original hash index, and adding successive values of an arbitrary quadratic 
+polynomial until an open slot is found.
+
+This helps to avoid primary clustering of entries (like in Linear Probing), but might still result in secondary
+clustering where keys that hash to the same value probe the same alternative cells when a collision occurs.
+
+h(k, i) = ( h`(k) + c1 * i + c2 * (i^2) ) mod m  where c1 and c2 are arbitrary constants
+
+### Double Hashing
+This is a method of probing where a secondary hash function is used for probing whenever a collision occurs.
+
+If h2(k) is relatively prime to m for all k, Uniform Hashing Assumption can hold true, as all permutations of probe 
+sequences occur in equal probability.
+
+h(k, i) = (h1(k) + i * h2(k)) mod m where h1(k) and h2(k) are two ordinary hash functions
+
+*Source: https://courses.csail.mit.edu/6.006/fall11/lectures/lecture10.pdf*
+
+## Analysis
+let α = n / m where α is the load factor of the table
+
+For n items, in a table of size m, assuming uniform hashing, the expected cost of an operation is:
+
+<div style="text-align: center;">1/1-α</div>
+
+e.g. if α = 90%, then E[#probes] = 10;
+