What is pattern matching?
In computer science, pattern matching is the act of checking a given sequence of tokens for the presence of the constituents of some pattern. In contrast to pattern recognition, the match usually has to be exact: "either it will or will not be a match." The patterns generally have the form of either sequences or tree structures. Uses of pattern matching include outputting the locations (if any) of a pattern within a token sequence, to output some component of the matched pattern, and to substitute the matching pattern with some other token sequence (i.e., search and replace).
Sequence patterns (e.g., a text string) are often described using regular expressions and matched using techniques such as backtracking.
Tree patterns are used in some programming languages as a general tool to process data based on its structure, e.g. C#, F#, Haskell, ML, Python, Ruby, Rust, Scala, Swift and the symbolic mathematics language Mathematica have special syntax for expressing tree patterns and a language construct for conditional execution and value retrieval based on it.
Often it is possible to give alternative patterns that are tried one by one, which yields a powerful conditional programming construct. Pattern matching sometimes includes support for guards.
Pattern matching is used to determine whether source files of high-level languages are syntactically correct. It is also used to find and replace a matching pattern in a text or code with another text/code. Any application that supports search functionality uses pattern matching in one way or another.
What is pattern matching in regular expression?
A regular expression (sometimes called a rational expression) is a sequence of characters that define a search pattern, mainly for use in pattern matching with strings, or string matching, i.e. “find and replace”-like operations
Regular expressions allow you to select specific strings from a set of character strings. The use of regular expressions is generally associated with text processing.
Regular expressions can represent a wide variety of possible strings. While many regular expressions can be interpreted differently depending on the current locale, globalization features provide for contextual invariance across locales.
Pattern matching is used by the shell commands such as the ls command, whereas regular expressions are used to search for strings of text in a file by using commands, such as the grep command.
What is a pattern matching algorithm?
Pattern matching algorithms are the algorithms that are used to figure out whether a specific string pattern occurs in a string text. Two of the most widely used pattern matching algorithms are the Naive Algorithm for pattern matching and the pattern matching algorithm using finite automata.
However, these are not the only two pattern matching algorithms available. There are several solutions to the pattern matching problem that have been proposed. These pattern matching algorithms have been classified into online and offline solutions.
Online solutions happen to be dynamic and they do not need a priori knowledge of the pattern database, preprocessing could be carried out on the pattern. Online solutions generally have two phases: the preprocessing phase of the pattern, and the search phase of the pattern in the pattern database. While the preprocessing phase is going on, a data structure is built which tends to be proportional to the length of the pattern and details vary for various algorithms. The search phase makes use of the data structure and attempts to quickly figure out whether the pattern appears in the text. This phase is generally based on four separate approaches. These include the classical approach, suffix automata approach, bit-parallelism approach, and the hashing approach.
Offline solutions are based on preprocessing activities that are carried out on the patterns database to prepare for the matching process.
How does pattern matching work?
The Pattern Matching process looks for a specified pattern within a user-defined value. You can use Pattern Matching to recognize social security numbers, telephone numbers, ZIP codes, or any other information that follows a specific pattern. It is also useful for looking for information that follows leading text: for example, looking for "Name:" and then extracting the text that comes after it.
Another use is in reprocessing documents; for example, extracting a piece of information such as the date from a file name and entering it in a field. Pattern Matching works by "reading" through text strings to match patterns that are defined using Pattern Matching Expressions, also known as Regular Expressions. Pattern Matching can be used in Identification as well as in Pre-Classification Processing, Page Processing, or Storage Processing.
What is pattern matching in ML?
Machine learning uses mathematics, statistics, and domain-specific knowledge and data to solve complex problems. Machine learning is turning data into numbers and finding patterns in those numbers.
For finding patterns, algorithms are used. An algorithm is a specific set of steps to perform a task.
An algorithm in machine learning is a procedure that is run on data to create a machine learning model. A machine learning algorithm is written to derive the model. The model identifies the patterns in data that fit the dataset.
A model represents what was learned by a machine learning algorithm. It is basically a mathematical function that can adapt to new data by tweaking its parameters.
What is the importance of pattern matching?
Pattern matching has many useful applications, such as:
- Natural Language Processing: Applications like spelling and grammar checkers, spam detectors, translation, and sentiment analysis tools heavily depend on pattern recognition methods. Regular Expressions are helpful in identifying complex text patterns for natural language processing.
- Image processing, segmentation, and analysis: Pattern matching is used to give human recognition intelligence to machines which are required in image processing.
- Computer vision: Pattern matching is used to extract meaningful features from given image/video samples and is used in computer vision for various
- applications like biological and biomedical imaging. Tumor identification is a classic example.
- Seismic analysis: The pattern matching approach is used for the discovery, imaging, and interpretation of temporal patterns in seismic array recordings. Statistical pattern recognition is implemented and used in different types of seismic analysis models.
- Radar signal classification/analysis: Pattern matching and signal processing methods are used in various applications of radar signal classifications like landmine detection and identification.
- Speech recognition: The greatest success in speech recognition has been obtained using pattern matching paradigms by treating each word as a unit.
- Fingerprint identification: Many fingerprint recognition methods are in use to perform fingerprint matching out of which pattern matching approaches is widely used.