Data Cleaning in Data Mining Simplified 101

Muhammad Faraz • Last Modified: January 18th, 2023

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Data Cleaning in Data Mining process is an important step. It plays a crucial role in the construction of a model. Data cleaning is a necessary activity, yet it is frequently overlooked. The key difficulty in quality information management is data quality. Data quality issues can arise in every information system. Data cleansing solves these issues.

Fixing or deleting incorrect, corrupted, improperly formatted, duplicate, or incomplete data from a dataset is known as data cleaning. If the data is inaccurate, the results and methods are untrustworthy, even if they appear to be correct. There are numerous ways for data to be duplicated or mislabeled when merging multiple data sources.

Data Cleansing, in general, lowers the mistakes and improves data quality. Correcting data inaccuracies and removing incorrect entries can be a time-consuming and laborious procedure, but it cannot be avoided. Data Mining is an important tool for cleansing data. Data Mining is a technique for locating relevant information in large amounts of data.

Data Mining is a relatively new strategy that employs data mining techniques to detect and recover data quality issues in huge databases. Data Cleaning in Data Mining mechanically pulls hidden and intrinsic information from data sets.

This blog will help you gain insights on Data Cleaning & Data Mining and the simple steps to get started with.

Table of Contents

What is Data Mining?

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Data Mining refers to the process of extracting useful information from data in order to inform corporate decisions and strategies. Exploring and analyzing vast blocks of data to find relevant patterns and trends is known as data mining. It can be used for a variety of purposes, including; detecting fraud, spam email screening, credit risk management, database marketing,  and even determining user attitude.

Big Data and Advanced Computing methods such as Machine Learning and other forms of Artificial Intelligence are used in Data Mining. The idea is to uncover patterns in otherwise unstructured or massive data sets that can lead to inferences or predictions. However, before data mining can begin, it is necessary to invest time in cleaning data.

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What is Data Cleaning?

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Before analyzing data, it is necessary to remove faulty data, structure the raw data, and fill in the null values. Finally, data cleaning prepares the material for data mining, which pulls the most important information from the data. Data mining is usually analytical; data cleaning allows the user to uncover erroneous or incomplete data prior to business analysis and insights.

In most circumstances, data cleaning in data mining is a time-consuming procedure that necessitates the use of IT resources to assist with the first data evaluation.

How to Get Started with Data Cleaning in Data Mining?

While data cleaning processes differ depending on the sorts of data your firm stores, you may utilize these fundamental steps to create a foundation for your company. Below are the simple steps to get started with:

Step 1: Removing Unwanted or Irrelevant Observations

Data Cleaning in Data Mining helps you to remove any unnecessary data from your dataset, such as duplicates or irrelevant observations. Similar data is most likely to occur during the data collection process. Similar data can be created when you integrate data sets from different sources, scrape data, or get data from customers or multiple departments. One of the most important aspects to consider in this procedure is de-duplication.

You’ve made irrelevant observations when you notice things that aren’t related to the problem you’re seeking to address. If you want to investigate data regarding millennial clients but your dataset also contains older generations, you might want to exclude such observations. This can speed up analysis and reduce distractions from your main goal, as well as provide a more manageable and performant dataset.

Step 2: Fixing Structural Error

Structural errors are errors that occur during measurement, data transfer, or other comparable situations. TStructure problems include typos in feature names, the same attribute with different names, wrongly labeled classes, i.e. separate classes that should be the same, and uneven capitalization. For example, the model will treat America and America as distinct classes or values, despite the fact that they represent the same value, or red, yellow, and red-yellow as distinct classes or characteristics, despite the fact that one class might be contained in the other two. So, there are some structural flaws in our model that cause it to be wasteful and produce bad results. 

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Step 3: Filtering Unwanted Outliers

There will frequently be one-off observations that do not exactly fit within the data you are studying at first sight. If you have a good cause to delete an outlier, such as incorrect data entry, it will make the data you’re working with performing better. The advent of an outlier, on the other hand, can sometimes prove a theory you’re working on. Keep in mind that just because an outlier occurs does not indicate it is erroneous. This step is essential to determine the legitimacy of the number. If an outlier appears to be unimportant for analysis or is a mistake, consider eliminating it.

Step 4: Handling Missing Data

Missing data is a confounding problem in Machine Learning. We can’t just disregard or eliminate the missing observation. They must be handled with caution because they may indicate something significant.

  •  Dropping data with missing values is one of the most prevalent strategies to deal with missing data. The absence of the value could be instructive in and of itself. Furthermore, in the actual world, you will frequently be required to generate predictions based on new data, even if some features are lacking.
  • Using data from previous observations to fill in the gaps. If a value is missing, you should alert your algorithm. Even if you create a model to infer your values, you aren’t adding any actual data. You’re simply reiterating the patterns established by prior features.

Missing data is similar to missing a piece of a puzzle. Dropping it is the same as assuming the puzzle slot doesn’t exist. If you infer it, you’re attempting to fit a component from another jigsaw into the puzzle.

As a result, missing data is usually instructive and indicative of something significant. We must also be informed of our missing data algorithm by flagging it. You’re basically enabling the algorithm to calculate the ideal constant for missingness rather than filling it in with the mean by employing this flagging and filling technique.

Step 5: Validating

As part of basic validation, you must be able to answer these questions at the end of the data cleansing process:

  • Is this data making some sense?
  • Does it provide enough evidence of your working theory, or bring any insight to light?
  • Can you spot patterns in the data to aid in the development of your next hypothesis?
  • Is this due to a problem with data quality?

Poor company strategy and decision-making might be informed by false conclusions based on erroneous or “dirty” data. False conclusions can result in a humiliating moment in a reporting meeting when you learn your data doesn’t hold up under inspection. It’s critical to establish a data-quality culture in your organization before you get there, hence, Data Cleaning in Data Mining is a must.

To do so, you should document the tools you might employ to foster this culture as well as your definition of data quality.

Benefits of Data Cleaning in Data Mining

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Having clean data will ultimately enhance overall productivity and allow you to make the best decisions possible. Here are some of the primary advantages of Data Cleaning in Data Mining:

  • Duplicates will be removed: When you collect data from multiple sources or scrape data, it is possible that you may have duplicate entries. These duplications could be the result of human error, such as when entering data or filling out a form.
  • Removes Irrelevant Information: Irrelevant data will stymie and complicate any analysis you do. So, before you begin data cleansing, you must first determine what is significant and what is not.
  • Correct Errors: It almost goes without saying that any inaccuracies in your data must be thoroughly removed. Typos, for example, can cause you to lose out on important data insights. Some of these can be prevented by performing a basic spell-check.
  • Formatting Clarity: Machine Learning models cannot handle material that has been highly structured. If you’re gathering information from several sources, it’s possible that there will be a variety of document types. This can lead to data that is misleading and erroneous.
  • Capitalization will be Standardized: You must ensure that the text in your data is consistent. If you use a mix of capitalization, you may end up with a variety of erroneous categories.


So far, we’ve gone over five distinct data cleaning methods that can help data cleaning in data mining be more dependable and generate good outcomes. We’ll have a strong dataset that avoids many of the most common errors after successfully completing the Data Cleaning stages. This stage should not be skipped because it is crucial for Data Cleaning in Data Mining and for the rest of the process.

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