Data Mining – What is Data Mining Process, Techniques and Examples?

What is Data Mining?

• Data Mining is the process of sorting large data sets to find relevant and valuable information for a specific purpose.
• As a sub discipline of computer science, Data Mining focuses primarily on patterns.
• After the data has been obtained and stored, the next step focuses on its interpretation; otherwise, it would be a pointless task of Data Mining.
• Data analysis uses concepts like machine learning.
• In which complex adaptive algorithms analyze data artificially.More traditional methods involve the participation of data scientists (experts specifically trained to interpret complex information).
• Who is responsible for generating reports for management to make decisions.

Knowledge management for customers

Knowledge Management for Customers refers to the tools and processes that a company uses to capture, store, organize, access, and analyze data about its customers, for the purposes of enhancing its sales, retention, and engagement efforts.

What uses can be given to data mining?

• Data mining used for many purposes, depending on each company and its needs.

Some of Data Mining possible uses include the following:

Forecasts and risks:

• Analyzing data to determine the source of past mistakes
• For example, the number of web visitors who did not purchase a particular item after browsing it
• Similarly determining the time of day that a system experienced web traffic overload in the past.
• It can help by allocating more resources or investing in server upgrades.

Grouping:

• Customer-supplied data allows companies to group users in many ways.
• For example, demographically based on gender, age, income, where they live, and their spending habits.

Behavioral Analysis:

• Examining the data allows companies to understand the type of stimuli.
• Do certain groups respond to specific offers or emails at a particular time of day or on a specific day of the week.
• Data Mining is the process of databases to find helpful information for decision-making.
• The fundamental basis for this is that these patterns help decision-making.
• For example, it could help companies understand their customers’ behavior patterns to facilitate the establishment of strategies to increase sales or reduce costs.

Advantages of Data Mining:

• The fundamental advantage of this data analysis process is the large number of business scenarios.

Prediction:

• Forecast of the company’s sales.

Probability:

• Selection of the best clients for direct contact either by phone or email.

Sequence analysis:

• Analysis of the products that customers have bought and check the interrelation between them.

What are different Stages of Data Mining?

• Within a Data Mining process, we can find five phases:

Objective and data collection:

• First of all, we focus on what type of information we want to obtain.
• Let’s imagine the example that a supermarket wants to know what time of day is where there is more customer attendance.
• Data Mining would be the objective and the information that the trade wants to obtain in this case.

Data processing and management:

• Once we know the data we want to collect, we put the data to work.
• Once the sample is selected

Model selection:

• It is closely related to the previous phase. It is about creating a model or algorithm that gives us the best possible result.
• Data becomes a complicated task since it will depend on the type of information to be analyzed.
• Therefore, data miners carry out different tests of the algorithm.
• Such as linear regression, decision tree, time series, neural network, etc.

Analysis and review of results:

• Basically, it analyzes the results to check if they yield a logical explanation.
• An explanation that facilitates decision-making based on the information provided by the results.

Model update:

• The last step in the process would be the model update.
• We can do it over time so that it does not become obsolete.
• The model variables can also become insignificant, and therefore a periodic control of the model is required.

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