Data Quality in Data Preprocessing for Data Mining

By: Prof. Dr. Fazal Rehman | Last updated: February 3, 2024

1. Accuracy:

Data can be correct or wrong and accuracy refers to the correctness of the data.

2. Completeness:

Data entries can be completely or partially entered in the database and Completeness evaluates the extent to which data is recorded without missing values.

3. Consistency:

Example#1 of data consistency:

Table 1: Dataset with consistent Date Formatting Table 2: Dataset with Inconsistent Date Formatting

Example#2 of data consistency:

Table 1: Original Dataset with Consistent Gender Representation Table 2: Inconsistent Gender Representation

4. Timeliness:

Timeliness assesses whether the data is up-to-date. It is essential for data that change over time to reflect the changes.

5. Believability:

How trustworthy the data are correct?

6. interpretability:

how easily the data can be understood?