A fuzzy matching process between two datasets is carried out via a fuzzy matching technique using approximate string matching. Combining data that doesn’t exactly match via a fuzzy join is very effective. This can be immensely helpful and time-efficient when you want to quickly connect data without completing data going through a lengthy process that would force the keys to match.
Fuzzy matching techniques use the distance factor between values in a dataset combined with the features of the value, such as text, numeric, point, and more. It can help you streamline matching practices and make daily work easier.
Here are some important fuzzy name-matching techniques you must learn about.
Common Key Method
These techniques boil down names to a key or code based on how they sound in English, so names with similar sounds are assigned to the same key. There are many other techniques that also use Metaphone and Double Metaphone for Fuzzy name matching.
These techniques use phonetic algorithms to combine similar-sounding names into a single key and find related names. It employs a fixed-length key, but Metaphone uses a wider range of English pronunciation rules and allows for variable key lengths.
This method can be computationally demanding. It may not be able to handle names that the system is unaware of. In addition, it cannot handle names whose components have extra or missing spaces or are split across many fields. Hence, there are many limitations to consider.
The other problem is that the processing times can be long. Each name component is listed with all potential spelling variations, and matching names are sought after from these lists of name variations. Hence, this method is rarely recommended to those who must process large values every day.
Edit Distance Method
This method is limited to Latin-based languages to weigh swaps equally. A non-Latin script name must also be translated first, just like with the common key technique otherwise you may not be able to achieve the desired results. This method examines the number of character transitions needed to go from one name to another.
The coefficients are techniques that compare two names character by character. These methods consider a combination of two elements, including the number of similar characters and the number of edit operations required to change one name into another.
Statistical Similarity Methods
A statistical method uses hundreds or even thousands of matching name pairs to train a model to recognize what two “similar names” look like. The model then takes two names and assigns a similarity score. It is highly accurate and can directly match names written in multiple languages without transcribing them to Latin script.
Since gathering the matched names involves significant resources, this method has a higher entrance barrier. However, the accuracy might make it a great option. In high-transaction scenarios, a system that exclusively uses the statistical method to comb through millions of names in search of matches may be too sluggish to be practical.