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Last Updated: Mar 27, 2024
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The Data Mining Process

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Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM


What does data mining mean? It is pretty clear from its name itself. Data mining means extracting valuable business information from an extensive database. Like valuable minerals are extracted(mined)from deep below the Earth; similarly, important information is searched from a vast database. Hence the name. With a massive lurch in the volume of data multiplying over the last few years, it is high time to structure the unstructured data that dominates 90% of the digital universe. Data mining is a technology that will help you structure the data based on different properties like demand, quality, and quantity.

Data Mining Process

Data mining refers to a technology that involves the mining or the extraction of knowledge from extensive amounts of data. Data Mining is the computational procedure of locating patterns in massive data sets involving artificial intelligence, machine learning, statistics, and database systems. The main aim of the data mining process is to extract information from a data set and translate it into an understandable structure to be used in the future. The fundamental properties of data mining are Automatic discovery of patterns, Prediction of likely outcomes, Creation of actionable information and Focus on large datasets and databases. 

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Steps in Data Mining Process

The data mining process is split into two parts: Data Preprocessing and Mining. Data Preprocessing involves data cleaning, integration, reduction, and transformation, while the mining part does data mining, pattern evaluation, and knowledge representation of data. 

1. Data Cleaning

The first and foremost step in data mining is the cleaning of data. It holds importance as dirty data can confuse procedures and produce inaccurate results if used directly in mining. This step helps remove noisy or incomplete data from the data collection. Some methods can clean data themselves, but they are not robust. Data Cleaning carries out its work through the following steps: 

(i) Filling The Missing Data: The missing data can be filled by various methods such as filling the missing data manually, using the measure of central tendency, median, ignoring the tuple, or filling in the most probable value.

(ii) Remove The Noisy Data: Random error is called noisy data. This noise can be removed by the method of binning.

  • Binning methods are applied by sorting all the values to bins or buckets.
  • Smoothening is executed by consulting the adjacent values. 
  • Binning is carried out by smoothing of bin, i.e., each bin is replaced by the mean of the bin. 
  • Smoothing by a median, a bin median replaces each bin value. Smoothing by bin boundaries, i.e., the bin's minimum and maximum values are bin boundaries, and the closest boundary value replaces each bin value.
  • Then, identifying the outliers and solving inconsistencies.

2. Data Integration

When multiple data sources are combined for analysis, such as databases, data cubes, or files, this process is called data integration. This enhances the accuracy and speed of the mining process. There are different naming conventions of variables for different databases, causing redundancies. These redundancies and inconsistencies can be removed by further data cleaning without affecting the reliability of the data. Data Integration is performed using migration Tools such as Oracle Data Service Integrator and Microsoft SQL.

3. Data Reduction

This technique helps obtain only the relevant data for analysis from data collection. The volume of the representation is much smaller while maintaining integrity. Data Reduction is performed using Naive Bayes, Decision Trees, Neural networks, etc. Some strategies for the reduction of data are:

  • Decreasing the number of attributes in the dataset(Dimensionality Reduction)
  • Replacing the original data volume with more minor forms of data representation(Numerosity Reduction)
  • The compressed representation of the original data(Data Compression).

4. Data Transformation

Data Transformation is a process that involves transforming the data into a form suitable for the mining process. Data is merged to make the mining process more structured and the patterns easier to understand. Data Transformation involves mapping of the data and a code generation process.

Strategies for data transformation are: 

  • Removal of noise from data using methods like clustering, regression techniques, etc. (Smoothing).
  • Summary operations are applied to data(Aggregation).
  • Scaling of data to come within a smaller range(Normalisation).
  • Intervals replace raw values of numeric data. (Discretization) 

5. Data Mining

Data Mining is the process of identifying intriguing patterns and extracting knowledge from an extensive database. Inventive patterns are applied to extract the data patterns. The data is represented in patterns, and models are structured by classification and clustering techniques

6. Pattern Evaluation

Pattern Evaluation is the process that involves identifying interesting patterns representing the knowledge based on some measures. Data summarization and visualisation methods make the data understandable to the user.

7. Knowledge Representation

Data visualisation and knowledge representation tools represent the mined data in this step. Data is visualised in the form of reports, tables, etc.

Also check out - Phases of Compiler

Frequently Asked Questions 

  1. What are the steps in the data mining process?
    There are seven steps in the data mining process: Data Cleaning, Data Integration, Data Reduction, Data Transformation, Data Mining, Pattern, Evaluation, Knowledge Representation.
  2. What is data mining?
    Data mining refers to a technology that involves the mining or the extraction of knowledge from extensive amounts of data. 


In this article, we have extensively discussed Data Mining technology and its processes in detail. We hope that this blog has helped you enhance your knowledge, and if you wish to learn more, check out our Coding Ninjas Blog site and visit our Library. Do upvote our blog to help other ninjas grow.

Happy Learning!

Topics covered
Data Mining Process
Steps in Data Mining Process
1. Data Cleaning
2. Data Integration
3. Data Reduction
4. Data Transformation
5. Data Mining
6. Pattern Evaluation
7. Knowledge Representation
Frequently Asked Questions