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How Can You Accurately Assess The Quality Of Data
The purpose of Data Mining is well known or everyone. One of the major problems that arise with Data Mining is that there will be several quality issues in the data. The technique which we follow to address the Data Quality issues is known as Data Cleaning.
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Why Data Cleaning Is Crucial?
We cannot overlook the fact that most of the data which is collected from the Data Mining process would surely be of good quality & is ready for analysis. Owing to various reasons, the collected data may be incorrect or unrelated to the problem which has to be addressed. Human error, limitations of measuring devices, or flawed data collection process are some of the reasons for that could lead to the collection of data of poor quality.
Poor quality of data relates to missing values from the data sets, missing of data objects or there are redundant/duplicate data objects or even corrupted data.
Let’s discuss about some of the best strategies for handling missing data.
Eliminate Data Objects or Attributes-
One of the best approach to address issues related to missing data is by simply eliminating data objects with missing values. In case if the data set is having not more than a few missing values, then we can simply omit them as they aren’t going create much of a difference. However, while eliminating the data sets with missing values, we should see that the eliminated attributes aren’t going to be critical for the process of analysis.
Estimate Missing Values-
One of the recommending approaches for dealing with missing data is to make accurate estimation about it which is done based on a various factors. We can always take the average of that attributes in place of missing values if the attribute is continuous in nature.
Duplicate Data-
This is the most frequently occurring problem. There could be several duplicate values in the collected data sets & eliminating these duplicates is quite a challenging task. While deleting the duplicates we should take care not to combine data objects that are similar, but not duplicates.
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