Data Mining Techniques Examples_and Definition


You might have heard about data mining, but what is data mining? IT companies such as Google, Facebook, and Twitter collect information from their customers to fuel their latest AI algorithms. It is important to know whether a company wishes to recognize its target consumer groups, boost revenue, reach a new market, or re-enhance its strategic position. This Data Mining discussion will go into what is data mining, data mining meaning, its techniques, its advantages, and other similar topics such as data science and data clustering.

Definition of Data Mining

Analyzing secret data patterns into meaningful data gathered and saved for productive processing in database warehouses can be referred to, as the data mining definition. The data mining algorithms facilitate business decision-making, and other knowledge needs to minimize costs and raise revenues eventually. Data mining requires efficient data acquisition, storage, and device treatment. This brief explanation may not have cleared your doubts on what is data mining, so let’s take a deeper look.

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What is Data Mining?

Data mining is the practice of identifying high-value trends in large amounts of data. It is a multidisciplinary capability that extracts knowledge from machine learning, statistics, and AI to determine the likelihood of future events. Data Mining insights are used for the detention of fraud, marketing,  science discovery, etc. Knowledge Discovery of Data (KDD), knowledge retrieval etc. 

Now that you have understood what is data mining, let’s explore related concepts.

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Data Mining Process

Identify the issue: Determine the nature and goals of the data scanning project’s business challenge.

Explore the data: This phase involves scanning and collecting data to resolve the company issue.

Data preparation: To schedule it for more modelling procedures, clear and organize the data gathered.

Modelling: Build a model using techniques of data mining to solve the problem indicated.

Output interpretation and assessment: Draw assumptions and test their relevance from the data model. Translate the outcome into a company option.

Data Mining Techniques

The following are the most widely used methods in the field:

   Anomaly detection is the process of identifying odd values in a dataset.

   Dependency modelling is the process of discovering established relationships in a dataset. Regression processing is commonly used in this context.

   Clustering is the process of identifying structures (clusters) in unstructured files.

   Classification is the process of generalizing a defined structure and applying it to results.

Data Mining Tool

The following are the two common data mining tools that are commonly used in the industry;

    R-language: The R programming language is a free and open-source platform for mathematical computation and graphics. R includes many mathematical, classical statistical measures, time-series analysis, grouping, and graphical techniques. It provides efficient data handling and storage.

   Data Mining with Oracle: Oracle Data Mining, abbreviated ODM, is an Oracle Advanced Analytics Database module. This data mining platform enables data analysts to produce detailed analyses and forecasts. It aids in the prediction of customer behaviour, the creation of customer profiles, and the identification of cross-selling opportunities.

Advantages of Data Mining

Some of the advantages of data mining are;

   Data mining lets businesses obtain intelligence dependent on experience.

   Data mining assists companies in making profitable operational and output modifications.

   Compared to other predictive data applications, data mining is a cost-effective and reliable approach.

   The method of decision-making is assisted by data mining.

   Facilitates automatic trend and activity prediction and automated hidden pattern detection.

   It can be deployed in both modern and current networks

   It’s the quick method that makes analyzing large amounts of data possible for consumers with less time.

Data Mining Examples

Let us see what some of the data mining  examples are:

Example 1

Take a telecom service marketing manager who needs to boost large sales. Customer profiling is critical for high ROI in its sales and marketing activities. It has a wide range of data such as age, ethnicity, salary, credit history, etc. However, the profiles of people who choose long-distance calls using manual research cannot be determined. He can detect similarities between long-distance call users and their characteristics by using data mining techniques.

For example, he might learn that his best clients, who receive more than $80,000 a year, are married women between 45 and 54 years. You can tailor such millennial marketing campaigns.

Example 2

A bank is looking at new means of increasing credit card sales. If fees are halved, they would like to check if consumption would double. Bank has recorded average card balances, payment amounts, credit limit use, and other primary parameters for many years. They develop a model to verify the effect of the current market strategy proposed. The data indicate that halving costs for a target consumer base could raise sales by 10 million dollars.

These examples give you a good grasp of what is data mining.


Now that you have understood what is data mining is, you should also know that they are used in diverse industries such as Banking, Communications, Education, Insurance, Manufacturing, Retail, eCommerce, Supermarkets, Bio-informatics, etc. Completing Online courses for data science from Jigsaw Academy help you get knowledge-based information and evaluate how consumers create marketing campaigns that reduce costs and increase sales.

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