Whenever you shop online from E-commerce websites, you must have found tags such as “people also viewed”, “Frequently bought together”, etc. These tags help you find similar or popular items in that category. Have you ever imagined how the websites provide that data to you? The website does this beautifully and accurately with the help of Data Mining.
If we have a look at the Data Mining definition, it tells us the discovery of hidden relationships among data lying in massive data sets. So, in this article, we will try to look at the fundamentals of Data Mining and learn a few key factors.
What is Data Mining?
Mining means extracting useful information from large sets. Similarly, Data Mining is a process where a large number of data sets are analyzed to extract patterns with the help of Artificial Intelligence, Machine Learning, and Statistics. The patterns are then used in decision-making and predicting future possibilities of events.
Types of Data
We need large amounts of data to procure data mining techniques and algorithms. However, there are certain types of data where data mining techniques are best noticed.
Some of the most commonly used data types are Relational databases, Multimedia streaming databases, Data warehouses, Text databases, Advanced databases, object-oriented databases, and transactional databases.
Data Mining Process
As mentioned earlier, Data Mining deals with a considerable quantity of data. Therefore, it is necessary to understand how to process the data correctly. Although there are Data Scientists specifically working in these sectors, large industries or individuals familiar with relevant data types can also Mine data. In any case. Data Mining goes through four processes. They are-
This process is used for storing data from various data pools in a repository. Here, both unstructured and structured data are saved in a Data Warehouse.
Data-quality checks and pre-processing of data take place in this phase before its ready for mining. Miners explore the stored data and fix any errors before moving on.
Prepared data are mined using techniques and applying predefined algorithms. Miners use various tools embedded with pre-trained algorithms, prepared with sample data through Machine Learning.
Analysis & Interpretation
Mined data are then analyzed and prepared with Data Visualization for easy understanding. Miners work on generating analytical models that help the industry in their decision-making.
Data Mining Tools
There are way too many tools for Data Mining. Few of the famous and most used are –
SAS or Statistical Analysis System is great for analyzing big data and is avidly used by large business organizations. It has an intuitive Graphical User Interface (GUI) for non-technical users. It is one of the best, if not the best, data mining software available.
An AI Assistant-powered business analytics and intelligent platform, Zoho is among the popular data mining software available. Businesses or individuals can create custom dashboards and are presented with augmented analytics using AI, Machine Learning, and Natural Language Processing.
It’s another valuable tool for Data Scientists with an easy drag-and-drop feature. It allows users to create predictive models and source data from shallow pools.
Oracle BI is an open-source solution for Data Mining requirements by which users can add several external extensive data into this tool. Moreover, it allows add-on installation and data visualization.
Additionally, several other popular ones are available such as Teradata, R-Programming, RapidMiner, and Click.
Benefits of Data Mining
1. Effective Marketing: Through Data Mining, organizations can create a list of interested audiences or buy products related to theirs. That way, they can create a target audience, spend the budget accordingly and get more ROI.
2. More Functional Production: Companies with the results from data mining tools can analyze the present delays in the production cycle, thereby predicting the upcoming problems and preparing themselves beforehand. As a result, the production quantity increases with a seamless process.
3. Get rid of fraud: Data Mining has helped organizations identify market fraud. They can have quality data with fraudulent acts and behaviors through proper market analysis by Data Mining.
4. Great for decision making: With many options available currently, people may be confused about the ultimate purchase; through Data Mining, users can have the necessary information by which they can make the right decision.
Disadvantages of Data Mining
1. Irrelevant information: Data Mines deals with massive data sets with several parameters. If given too many presets, it may result in irrelevant information.
2. Complex Results: The result of a Data Mining action is very complex and has various parameters to analyze simultaneously. Therefore, you may need to learn Data Mining beforehand to use it.
3. Violates Privacy: Because it deals with users’ information and tries to predict its behavior, its tactics violate the fundamentals of users’ privacy.
Applications of Data Mining
Marketing: Create a target audience and generate more revenue by selling the item to interested customers.
Cybersecurity: Helps with all the cyber attack data and analyses patterns to create a prevention mechanism.
Medical Diagnosis: With the help of an abundance of medical records available, data mining helps in diagnosis and treatment.
Education: Analyze students’ records to correctly target them in their weaker subjects.
Research: It provides all the required information like weather conditions, environment state, etc., in research work in specific genres.
Data Mining has applications in different areas. Be it Banking, Insurance, Entertainment, Marketing, or other crucial areas. You can learn relevant data and take decisive action to improve quality, service, and security through it. The tools are well-equipped for performing a task and presenting visual information to make use. Are you familiar with any of them? Let us know!