## How does Count-Min Sketch work?

In computing, the countâ€“min sketch (CM sketch) is a probabilistic data structure that serves as a frequency table of events in a stream of data. It uses hash functions to map events to frequencies, but unlike a hash table uses only sub-linear space, at the expense of overcounting some events due to collisions.

## Where is Count-Min Sketch used?

Count-min sketch is used to count the frequency of the events on the streaming data. Like Bloom filter, Count-min sketch algorithm also works with hash codes. It uses multiple hash functions to map these frequencies on to the matrix (Consider sketch here a two dimensional array or matrix).

**Why is Count-Min Sketch better?**

Essentially, the Count-Min Sketch is doubling-up, allowing multiple events to share the same counter in order to preserve space. The more cells in the table, the more counters we store in memory, so the less doubling-up will occur, and the more accurate our counters will be.

**Do count-min sketches provide error guarantees relative to the answer?**

The Count sketch (Charikar, Chen and Farach-Colton) provides guarantees relative to the L2 norm of the data frequencies, while the CM sketch gives guarantees relative to the L1 norm, at the expense of a worse dependence on epsilon (1/epsilon^2 instead of 1/epsilon).

### What are sketching algorithms?

Sketching algorithms simplify the computational task by generating a compressed version of the original dataset that then serves as a surrogate for calculations. The compressed dataset is referred to as a sketch, because it acts as a compact representation of the full dataset.

### What is FM algorithm in big data?

Flajolet Martin Algorithm, also known as FM algorithm, is used to approximate the number of unique elements in a data stream or database in one pass. The highlight of this algorithm is that it uses less memory space while executing.

**What type of algorithm is required for analyzing streaming data?**

Event detection. Detecting events in data streams is often done using a heavy hitters algorithm as listed above: the most frequent items and their frequency are determined using one of these algorithms, then the largest increase over the previous time point is reported as trend.

**What is the main difference between standard reservoir sampling and Min wise sampling?**

What is the main difference between standard reservoir sampling and min-wise sampling? Reservoir sampling makes use of randomly generated numbers whereas min-wise sampling does not.

## What clustering algorithms are good for big data?

The most commonly used algorithm in clustering are partitioning, hierarchical, grid based, density based, and model based algorithms. A review of clustering and its different techniques in data mining is done considering the criteria’s for big data.

## What is the count-min sketch algorithm?

The Count-Min sketch is a simple technique to summarize large amounts of frequency data. Count-min sketch algorithm talks about keeping track of the count of things. i.e, How many times an element is present in the set. Finding the count of an item could be easily achieved in Java using HashTable or Map .

**What is a count-min sketch in C?**

A Count-Min Sketch implementation in C. Count-Min Sketch is a probabilistic data-structure that takes sub linear space to store the probable count, or frequency, of occurrences of elements added into the data-structure. Due to the structure and strategy of storing elements, it is possible that elements are over counted but not under counted.

**What is count-min sketch in Bloom filter?**

Count-min sketch is used to count the frequency of the events on the streaming data. Like Bloom filter, Count-min sketch algorithm also works with hash codes.

### How to use the count_min_sketch library?

To use the library, copy the src/count_min_sketch.h and src/count_min_sketch.c files into your project and include it where needed. To generic method to query the count-min sketch for the number of times an element was inserted is to return the minimum value from each row in the data-structure.