Rabin Karp Algorithm for Pattern Searching
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Programmers know how deep and vast the Data structure and algorithm concepts are. Even when one tries to comprehend everything, something new keeps getting found by tech geniuses.
Such a concept that is not popularly studied about and understood is the Rabin Karp Algorithm. When a user searches for a pattern, the rabin karp method plays a significant role in processing the request.
To help get you started on this topic, we’ve compiled all the necessary information to present you a full-fledged guide to maximize your knowledge on this topic. Let’s start reading!
About Rabin Karp Algorithm
When you want to find a particular item from a row or string of items, it is easy because we can look at it and pick it. But when the same need arises in the computer language, it requires a set of rules to follow. Such rules that help the computer to give the searched pattern to the user are filed by the rabin karp algorithm.
Rabin Karp uses the hash function to pick out the patterns that you search for in a string. Usually other methods that help us in finding a pattern might take loads of time because it scans every item of the string.
However, in the rabin karp algorithm, the duration of finding the required pattern is shortened significantly.
How is the duration of the search reduced? Certainly because of employing the hash function. Let’s check what a hash value means.
Hash Value
When a user wants to map a huge input into a small output, the hash value tool is implemented. Hash values further help users to find the data that is specifically needed without causing any conflict. As hash values resist collision, users cannot often find another hash value with the same information.
How does rabin karp calculate hash value?
The rabin karp algorithm uses the hash value to print a long string as a small integer. This process wouldn’t have been possible if not for the hash function. Usually programmers who know about Java language would have come across the syntax HashCode().
Usually when we use this Java syntax, we simply get an integer which is nothing but the input string’s hash value. But, rabin karp adds a special method. The hash value of the required pattern from the string is firstly found and then every hash value is compared to pick the finest hash value. The formula that is used by rabin karp to find the best hash value is: Σ(v * d^(m-1)) mod 13
In this formula, Σ denotes the sigma, v denotes the number values of the searched pattern, d denotes how many elements are in the given string, while m denotes the length of the string.
Shall we quickly look at this example to understand it?
Let’s take a string with the elements: W, X, Y, Z
And the pattern you are searching from this string is X Y
Hence, the count of X and Y that is 2, will be the “v”
Now let’s assign number values in the lexical order for all the elements. It will be,
W: 1
X: 2
Y: 3
Z: 4
Once it gets assigned, we should note that the d is 4 (number of elements present) and the m is also 4 (length)
Now we know,
v: 2
m: 4
d: 4
And so the code will be,
= Σ(2 * 4^(4-1)) mod 13
= Σ(2 * 64) mod 13
= Σ(128) mod 13
= 128 mod 13
= 11
The final answer would be 11 which is a prime number.
What is the algorithm’s complexity?
In general, unlike other algorithms, rabin karp’s complexity is decent. We will get a time complexity of O(m+n) but if there are any advanced cases, it can move to becoming O ((n-m+1)m. The latter time complexity is extremely complicated but it only arises when a worst scenario occurs.
Additionally, when only one comparison takes place for every substring text, the time complexity gets to be decreased as well. Rabin Karp, when compared to other methods, stands proficient in keeping the time complexity good.
Applications and Limitations of the Rabin Karp Algorithm
Here are some notable applications of the rabin karp to look at before we check its limitations.
When a user wants to search for many patterns at the same time, there isn’t a better algorithm than rabin karp to be used.
Rabin karp detects plagiarism instantly. Say you’ve pasted an essay on a plagiarism finding website that uses the rabin karp algorithm. It will perfectly analyze it and show you which words or lines are plagiarized and from where it is sourced from. The only exception is that rabin karp avoids punctuation similarities in the text.
Rabin karp works efficiently when a user is sure that their scenario is free of worst cases. Otherwise it can hike the time complexity.
For the purpose of exact pattern matching, rabin karp can be utilized.
Rabin karp is beneficial even in a case where the elements in the string and the required pattern are completely different.
As we saw the remarkable features and applications of the rabin karp, let us take a look at its limitations which are:
Single string searching won’t be possible as there are too many sought strings present.
The rabin karp isn't as good as Knuth–Morris–Pratt, Boyer–Moore string search algorithm, or other string searching algorithms because these methods are extremely fast even when worst cases arise.
Speaking of worse cases, the reason why it comes in is because of the presence of spurious hit. Spurious hit simply means the difference between the string and the pattern from which the hash value is derived.
Difference between Rabin Karp and Distributed Deadlock Detection Algorithm
Rabin Karp and Distributed Deadlock Detection algorithms are the two algorithms that are used often to solve advanced DSA problems. The main difference is that with the help of Rabin Karp, we can find the required pattern within a string of data. With the help of a distributed deadlock detection algorithm, we can spot deadlocks within transaction data.
Conclusion
Founded in 1987 by Michael and Karp, to date, the rabin karp algorithm is the most sought method for pattern searching. While there are other faster algorithms available, coders specifically choose rabin karp due to its effective formula and the procedure it follows.
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