Data Preprocessing Questions Medium
There are several methods used for handling duplicate records in data preprocessing. Some of the commonly used methods are:
1. Deduplication: This method involves identifying and removing exact duplicate records from the dataset. It can be done by comparing all the attributes of each record and removing the duplicates based on a specific criterion, such as all attributes being identical.
2. Fuzzy matching: Fuzzy matching is used when the duplicates are not exact but have slight variations. It involves using algorithms like Levenshtein distance or Jaccard similarity to measure the similarity between records and identify potential duplicates. Once identified, these duplicates can be merged or removed based on specific rules.
3. Record linkage: Record linkage is used when dealing with datasets from different sources that may have overlapping records. It involves comparing the attributes of records from different sources and identifying potential matches. Various techniques like probabilistic matching or deterministic matching can be used to determine the likelihood of a match and handle the duplicates accordingly.
4. Rule-based methods: Rule-based methods involve defining specific rules or conditions to identify and handle duplicates. These rules can be based on domain knowledge or specific requirements of the dataset. For example, if a dataset contains customer records, a rule can be defined to consider records with the same name, address, and phone number as duplicates.
5. Clustering: Clustering is a technique that groups similar records together based on their attributes. It can be used to identify potential duplicates by clustering similar records and then examining each cluster for duplicates. Once identified, duplicates can be merged or removed based on specific criteria.
It is important to note that the choice of method for handling duplicate records depends on the specific characteristics of the dataset and the requirements of the analysis or application.