What is count min sketch algorithm ?

 The Count-Min Sketch algorithm is a probabilistic data structure used for estimating the frequency of elements in a stream of data. It was introduced by Cormode and Muthukrishnan in 2005 as a memory-efficient and scalable approach to approximate counting.

The Count-Min Sketch consists of a two-dimensional array of counters, typically implemented as an array of hash tables. Each hash table corresponds to a different hash function. When an element is encountered in the stream, it is hashed multiple times using different hash functions, and the corresponding counters in the hash tables are incremented.

Here's a high-level overview of the Count-Min Sketch algorithm:

1. Initialization: Create a two-dimensional array of counters with dimensions width x depth, initially set to zero.
2. Hashing: Choose a set of k hash functions, each mapping elements to one of the counters in the array.
3. Incrementing: When an element is encountered in the stream, hash it using each of the k hash functions. Increment the corresponding counters in the array for each hash function.
4. Estimation: To estimate the frequency of an element, hash it using the same set of k hash functions. Retrieve the minimum value among the counters associated with the hashed positions.

The Count-Min Sketch provides an approximation of element frequencies in the stream. It guarantees that the estimated frequency is always greater than or equal to the actual frequency. However, due to collisions and limited counter sizes, there is a probability of overestimation. The accuracy of the estimation depends on the width and depth of the sketch.

Count-Min Sketches are particularly useful when memory usage is a concern or when processing large-scale data streams. They are commonly employed in areas such as network traffic monitoring, approximate query processing, frequency analysis, and streaming algorithms.