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 Clients refers to a company’s tools and processes to capture, store, organize, access, and analyze customer data to boost sales, holding, 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.