August 12, 2022

How Does Fuzzy Matching Work?

This article's goal is to convey everything you need to know about fuzzy matching work. I'll explain what fuzzy matching is and how it works, and then I'll explain the analysis of a fuzzy model based on machine learning. And, then I'll talk about the advantages and disadvantages of fuzzy matching.

Fuzzy matching is a technique that's often used in natural language processing (NLP), which is the field of computer science that focuses on the manipulation of digital texts. Basically, fuzzy matching allows you to match phrases or sentences in a document to specific search terms.

This article will show you how to do fuzzy matching using the Google Search Engine, and it will also provide some tips on how to use this powerful tool effectively.

What is Fuzzy Matching?

Fuzzy matching is a technique used to identify similar elements in a data set. The algorithm compares two strings and assigns a score to each string based on how similar they are. The closer the two scores are, the more similar the two strings are. Fuzzy matching can be used to match items in a data set based on their similarities. For example, you might use fuzzy matching to match customer records against a list of customer preferences. This would allow you to identify customers who have similar preferences, even if they don't have exact matches. Fuzzy matching can also be used to match items in a data set based on their similarities.

What Is the Process of Fuzzy Matching?

The Fuzzy Matching operator calculates the Levenshtein distance between a document and the query. It determines the score by comparing the Levenshtein distance between a document and a query to the Levenshtein distance between the other documents and the same query. The Fuzzy Matching operator then assigns a score based on the Levenshtein distance. The Fuzzy Matching operator assigns a score between 0 and 1, where a score of 1 means that the documents match exactly. For example, a query of “financial projections” and a document of “financial overview” both have a score of 1 because they match exactly. On the other hand, a query of “financial projections” and a document of “investment planning” only have a score of 0.8 because their Levenshtein distance is 0.9.

How Fuzzy Matching Works?

Fuzzy matching is a search technique that uses a set of fuzzy rules to compare two strings. The fuzzy rules allow for some degree of similarity, which makes the search process more efficient. The fuzzy matching process begins by creating a list of keywords that are to be searched for in the text. These keywords can be anything that you want to find, and they are not limited to the words that are in the text itself. After the keywords have been created, they are then used to create a fuzzy search query. This query is used to compare the text against a database of fuzzy matches. If there is a match, it will return the corresponding word from the text. If there is no match, it will return "no match found."

Example: Assume we have two data sets: one of existing customers and the other of prospects purchased from a company. We want to contact prospects in order to convert them into customers, but we don't want to touch existing customers. The issue in this circumstance is that we must remove our current customers from the prospect list. However, because there is unlikely to be a decent ID code to match between these two files, we must discover a means to link the data sets to other fields. We can utilize the name and address fields, but we often don't get strong matches between names and addresses representing the same person because they're spelled somewhat differently. For example, Andrew Main, 25 State St, will not connect with Andy Main, 25 State Street, in full.

This is when fuzzy matching comes into play. It is quite strong, allowing you to connect two data sets together. By specifying parameters to match the values, fuzzy matching can find non-identical duplicates of a data collection. They don't have to be identical because fuzzy matching employs algorithms to determine how similar words or phrases are.


Analysis of a Fuzzy Model Based on Machine Learning

Currently, the performance of different machine learning algorithms for fuzzing is less rigorously analyzed. Throughout this section, you will learn how fuzzing tests are conducted using a machine learning model. It summarizes the five following points:

  • Machine learning algorithm selection
  • Methods of pre-processing
  • Sets of data
  • Metrics for evaluation
  • Setting Hyperparameters
  • Machine learning algorithm selection

To detect hidden vulnerabilities, the fuzzing test uses machine learning's categorization capability. It is possible to significantly increase vulnerability detection efficiency by utilizing a vast number of known sample sets and program execution feedback. However, there are other contexts in which machine learning algorithms are used. Alternative algorithms can result in significant differences in results if used in the same situation. The fuzzer accepts hexadecimal text, source code, binary strings, and network packets as input. In addition to its complicated syntax, semantics, and logic, the PUT has complex semantics. Machine learning algorithms are difficult to choose for the complex environment of fuzzing tests.

Pre-processing Approach

Three types of preprocessing approaches are used in fuzzing: program analysis, natural language processing, and others. In program analysis, various types of information are extracted from a program, including stacks, registers, assembly instructions, jumps, program control flow graphs, abstract syntax trees, and program execution pathways. NLP uses advanced text processing techniques to find hidden meanings in input data, such as n-grams, count statistics, Word2vec, heat maps, and so on. Other methods include combining program analysis with natural language processing techniques, turning full documents or pdf objects into vectors, and developing unique algorithms.


The training data has the greatest influence on machine learning performance. Deep learning, in particular, can easily lead to over-fitting when the amount of data is insufficient. The datasets used for the machine learning algorithm-based fuzzing test in this study come from the following sources:

  • Web crawler
  • Fuzzing generation Self-construction
  • Public database

Web crawlers are regularly used data collection tools, particularly for widely used file types such as DOC, PDF, SWF, and XML. Conventional crawling methods can download files based on file extension filter conditions, magic bytes, and other signature approaches. The fuzzing generation process involves running a similar fuzzer, such as AFL, and collecting the resulting samples and their tag data over time. This approach can build datasets in multiple formats and satisfy the number of samples.

Metrics for Evaluation

The performance evaluation of fuzzing methods based on machine learning technology may be divided into two parts: the performance evaluation of the machine learning model and the vulnerability identification capacity. The classification metrics are used to evaluate the machine learning model. Accuracy and Precision are the most often used performance metrics, according to statistics, followed by Recall, Loss, FPR, and F-measure. Models perplexity is the least used FPR.

Hyperparameters Setting

In machine learning models, hyperparameters are not determined by training, but rather by artificial settings prior to training. The best way to improve learning performance and efficacy is to optimize hyperparameters and select the optimal set. The hyperparameters of the deep learning algorithm, including the number of layers, the number of nodes in each layer, the epochs, the activation function, and the learning rate, are primarily selected to complete the comparison. A neural network's accuracy and complexity are determined by the number of layers and nodes in each layer. It is likely that over-fitting will occur in layers with a large number of nodes. In the fuzzing scenario, there are a maximum of four layers and 128 or 256 nodes. Increasing epochs increases the neural network's weight update iterations, and the loss function curve moves from an unfitted to an over-fitted state. It is usually decided to use 50 epochs, but 40 will produce the best results. By choosing the right activation function, the neural network may be able to model expression more accurately and address problems the linear model can't.

Fuzzy Matching vs. Synonym Matching

A synonym is an alternative to a word with a “similar meaning”. For example, a user might search for the word “projections,” but another term, such as “figures” or “estimates,” may be more appropriate. Synonym matching is used to find documents that include alternative terms for the same concept. Therefore, with synonym matching, you need to know all the different words for a particular concept in order to find the right documents. On the other hand, fuzzy matching means that even if you don’t know the exact word or phrase, you still have a chance of finding the relevant documents. Unlike synonym matching, with fuzzy matching, you don’t need to know all the words for a concept. Fuzzy matching is based on a Levenshtein distance algorithm. It is a string metric that quantifies the amount of effort needed to transform one string into another, which is a very common technique in computer science.

Advantages of Fuzzy Matching

Fuzzy matching is a machine learning algorithm that uses a Levenshtein distance to match strings of text. It has several advantages over traditional matching methods, including the ability to handle misspelled words and partial matches. Additionally, fuzzy matching is often more accurate than classical matching when it comes to detecting complex patterns.

One of the benefits of fuzzy matching is that it can be used to match strings of text that are not entirely correct. For example, if you are trying to match a customer's name to a customer record, fuzzy matching can be used to determine which letters in the name are similar to the letters in the customer record. This type of matching is often more accurate than using a standard spelling checker.

Fuzzy matching is also effective when it comes to detecting patterns. For example, if you are looking for all the documents that mention "a meeting at 7 pm", fuzzy matching can be used to identify all the documents that mention "meeting" or "7 pm". This type of pattern detection is often more accurate than using a standard search engine.

Disadvantages of Fuzzy Matching

Fuzzy matching can be a powerful tool for search, but there are some disadvantages to consider. First and foremost, fuzzy matching is not always accurate or reliable. Second, fuzzy matching can be slow and difficult to use. Finally, fuzzy matching can lead to bias in results.

These three factors can lead to inaccurate or unreliable results, as well as biased outcomes. Fuzzy matching can be slow and difficult to use, which can make it difficult to find the right matches. Additionally, fuzzy matching can lead to false positives (matches that are actually not relevant) and false negatives (matches that are actually irrelevant). These issues can make fuzzy matching less effective than traditional search techniques.

Using Fuzzy Matching with Artificial Intelligence - A Brief Guide!

A lot of people who are looking out for a string matching solution may have stumbled upon fuzzy matching. It is a widely used technique that can also be integrated with artificial intelligence. Since most of the solutions today are moving towards automation, we decided on creating this article to explain what fuzzy matching is and the associated algorithms to get started with.

To learn more, read ahead…

Need of Artificial Intelligence for Fuzzy Matching

As we see, we have mentioned two entries in the above examples. It is because creating a code to handle two entries won’t be complex. But what if? There are multiple entries and huge volumes of documents to process.

This is where the need for Artificial Intelligence comes into play. An artificial intelligent model has the capacity to take the best and optimal route to solve a problem. Also, it encompasses added logic to identify whether the provided entries belong to the same person or not.


Name: John David; Address: Los Angeles, Age: 62, Contact No: 310-555-1234, Pincode: 90005

Now “John David” seems like a common name and it can be misspelled as “John Davis” as well. Another criterion of thinking can be that there can be more than one “John David” in Los Angeles with the same area code. However, even if in a rare case the age of these people matches, the contact numbers are bound to be different. Even if “John David” misspelled his name as “John Davis”, a confidence score can be generated to handle the situation manually or neglect it completely.

To apply such sort of logic where a machine can make its own decision and take the best route, artificial intelligence is required. With AI, a platform that uses fuzzy matching will be able to identify the differences and matches between two separate entries and generate a combined confidence score to suggest the system or the manual intervener to take necessary action.

AI (Artificial Intelligence) Algorithms for Fuzzy Matching

These are not essentially AI algorithms but algorithms that can be used with an AI model to train the machine learning or deep learning model for the developing software. These are:

Hamming Distance

The Least complicated way to calculate the dissimilarities for string matching. It compares two equally sized strings for example “CAT” and “KAT”. Hamming distance provides the number of characters that don’t match corresponding to the index of that character. For instance “0” is the index that didn’t match and “1” is the number of characters.

Levenshtein Distance

Levenshtein distance is a technique that is used to calculate the number of dissimilarities between two strings. To further stress, the technique focuses on substitution, deletion, and insertion for modifying one string to another. For example the levenshtein distance of “John Davis” and “John David” is one.

Damerau Levenshtein Distance

It is identical to levenshtein distance as the name suggests. The only difference is it also includes transposition to its added function.

N-Gram Based String Matching

In N-gram based matching, a string is basically broken into a dataset of two components. For example “John” will be broken down to “Jo”,”oh”, and ”hn”. All these two components will be matched to the corresponding strings to generate results.

BK Tree

It is the fastest algorithm for string matching. It utilizes two techniques i.e. Levenshtein Distance and Triangle Inequality.

It creates a BK tree of words from a given dictionary. For example let dictionary dic={some, same, soft, salmon, soda, mole}

As mentioned in a diagram below, the levenshtein distance between the two strings in the dictionary will be generated. The first string of the dictionary will be the root node by default. If the corresponding string matches with the root node, the corresponding string will be made the child node and further on. If no child matches the corresponding string then the corresponding will be made the new children for the root node.


Once the tree is prepared, a threshold point is selected to which the string matching will happen. This threshold point is determined using Triangle Inequality. As per Triangle Inequality:

Distance (A,B) + Distance(B,C)≥Distance(A,C) where A,B,C are sides of a triangle.

Here the three sides of the triangle represent query, parent, and child. Also levenshtein distance is used as edge length.

Bitap Algorithm

It is another very fast algorithm that uses bitwise operation. The algorithm searches for approximate matches from a substring for a given pattern. This equality is determined using the levenshtein distance. If the substring and the patter are at a provided distance of k then then they are considered equal otherwise not.

Soundex Algorithm

This algorithm works on the format of determining phonetics. It is used for strings that sound the same but have different spelling. For example “wood” and “would”.

Meta Phone and Double Meta Phone Algorithm

It is an upgraded version of the soundex algorithm. Although, it is used while determining more than two words that sound similar but have different spelling. For example flour, floor, flower.

Cosine Similarity

In this algorithm, the matching is done using the cosine angle. It is done by breaking the two matching strings into n-gram. For example “cat” and “kat”. If we consider “cat” as base string then it will be written as “111” and “kat” will be written as “011”.

Taking the number of 1’s and 0’s and putting them in the cosine formula:

Cos =(A .B )/AB

And the resultant will give the matching percentage.

Can you do Fuzzy Matching in Excel?

String matching and searching for relevant data is key to any company’s document processing operations. The use case is industry agnostic since almost every organization in any industry is generating tons of data. Solutions like Excel are important because they not only help in creating spreadsheets for that data but also sorts and segregates them using multiple formulas and filters.

Yet, a million dollar question is…”Is Excel capable of approximate string matching using techniques such as Fuzzy Matching?”. The shorter answer to this is “YES”. But the question that remains is how can it be done? What is the installation process?, system specs, and many others that we’ll cater to along the way.

Need of Fuzzy Matching in Excel

Use of Excel is imperative to any organization for handling the data. When the data crosses the threshold of ease of management, there can be multiple duplicate entries, spelling mistakes, abbreviations, synonyms, missing data, etc that are hard to handle.

To handle this situation, the feature of Fuzzy Matching was added to Excel. In Excel, this feature is provided as an add-on known as “Fuzzy Lookup”.


What is Fuzzy Lookup?

Fuzzy Lookup is an add-on created by Microsoft. It is used for performing fuzzy matching on text-based data in the Excel spreadsheets. Fuzzy Lookup can be used to identify and rectify similar rows within a table or it can be used to fuzzy join two similar rows in different tables.

How it helps?

Suppose, there are multiple entries in a table that refer to the same person. For instance “John Davis”, “Mr. John Davis”, and “John D.”. The Fuzzy Lookup add-on will be able to provide a matching score between these three entries helping the user determine whether they are one or not. It is done using default configuration and custom configuration for matching different entries that correspond to the same person i.e. address, contact, pincode, SSN, etc.

How to Install Fuzzy Lookup on Excel?

As stated on the Microsoft’s official website, below are the installation methods for Fuzzy Lookup on excel:

Prerequisites: Pre-installed Microsoft Excel 2007 or beyond.

The Usual Way (to install the latest version):

  • Visit the Microsoft website, using the link mentioned above.
  • Download the .exe file for Fuzzy Lookup.
  • Uninstall any previous version of Fuzzy Lookup.
  • Run the downloaded setup.exe file to complete the installation.

The Admin Way (to counter 0x80070659 error):

  • Right click on the downloaded setup file.
  • Click on Run as Administrator.
  • Read the license agreement and agree to it.
  • Fuzzy lookup will be installed on the system.

Troubleshooting Trusted Publisher Issue:

  • Open Excel.
  • Click on Options.
  • Click on Trust Center Settings.
  • Click on Add-ins.
  • Choose Require Application Add-ins to be signed by Trusted Publisher.

System Requirements of Fuzzy Lookup

The following are the prerequisites system requirements for a successful Fuzzy Lookup installation:

Operating System: Windows Vista or higher
Processor Speed: 1 GHz, and 1 core or higher
Hard Disk Space: 2 GB
Additional Requirements: .NET 4.5, VSTO 4.0

How to Get Started with Fuzzy Lookup?

In order to get started with fuzzy lookup, we need to first format the data in our sheets correctly.

Now follow the step by step instruction mentioned below:

  • Select the range of the table for which you want to perform fuzzy lookup.
  • Now click on the “insert tab” to choose another table for comparison and select “choose table”.
  • Now select the entire two tables one by one and name them.
  • Now go to the fuzzy lookup tab and click on the “fuzzy lookup” button.
  • Choose the column that needs to be matched.
  • Now to perform fuzzy lookup, click on the button between the left and right column on the fuzzy lookup interface.

In order to perform Fuzzy lookup similarity, the steps are similar. Although, the user needs to select the option in the output column. It will provide the % of similarity between the two chosen columns in the table.

Things to Remember while setting up Fuzzy Lookup

There are certain things that need to be kept in mind before performing fuzzy lookup between two tables. These are:

  • In order to get the best of fuzzy lookup, it is important to format the data beforehand for maximum utilization.
  • The similarity percentage criteria of fuzzy lookup will depend on the columns selected for matching. The percentage can vary because each and every column has different types of data.
  • In fuzzy lookup, a null cell will match to another null cell in the other table, therefore, it is important to select the range of the cell to avoid skewed results. The user can also set a default value to each and every remaining cell to avoid such conditions.
  • The user can choose between fuzzy matching or exact matching. These are two different features that users often get confused with.
  • Fuzzy lookup can only be performed on data that can be defined as string(unicode or non-unicode).
  • Try to widen the cell range during matching to get the most benefit of matching.
  • Always review the data that is received.

Bottom Line

Excel is amongst the most widely used Microsoft software. It is omnipresent across industries and has been catering to organizations since its inception. With the addition of fuzzy lookup, organizations are now able to sort missed or incorrect data with ease. It has allowed organizations to fasten their process of finding skewness in the spreadsheet empowering their information system and made them independent of other third-party tools to check the legitimacy of their data.


Fuzzy matching can help you find more accurate results even if you don’t know the exact words or phrases to use. That said, it is best used for exploring content and finding relevant documents that might not be included in strict Boolean search results. Keep in mind that fuzzy matching only works with full-text indexes. It doesn’t work with standard SQL WHERE clauses. Fuzzy matching does not always provide accurate results, so it is not a replacement for a more accurate Boolean search. Fuzzy matching is based on a Levenshtein distance algorithm. It is a string metric that quantifies the amount of effort needed to transform one string into another.

What we do?

We provide our in-house developed document processing automation solution to SMEs and Large enterprises. Our product VisionERA is an intelligent document processing platform that is capable of providing end-to-end automation for various document processing related use cases. With VisionERA, companies can easily process a range of structured to unstructured documents with minimal intervention. It allows them to perform multiple document processing operations such as data extraction, validation, logic application, storage, etc. with ease.

VisionERA is a proprietary platform that has an in-built AI-engine and deep learning model. This feature of VisionERA allows it to behave cognitively and intuitively. With VisionERA, companies can process their raw data to information for empowering their knowledge and decision making. Also, the data can be easily exported in multiple formats such as excel, CSV, etc. or it can be directly stored to your central repository using multiple downstream applications.

We use our own in-house fuzzy model for our intelligent document processing platform VisionERA. To learn more about it, click on the CTA below. You can also send us a query by using our contact us page!

Get Started with your Document Automation Journey

$0 Implementation cost | $0 monthly payments -> No Risk, No Headaches

Pay only for Satisfactory Results!

Sign up for Free Trial