Are you looking for data mining techniques? AAmcourses is here with the answers for you. In this article, we will study the various types of data mining techniques used for different kinds of data and processes.
Every business produces a large amount of data that they need to analyze. Analyzing the data provides insights about the business’s current situation and helps forecast the future position; that’s where data mining comes in.
Data mining is a tool that helps business to identify hidden trends and patterns emerging from the provided data. Data mining helps businesses select appropriate data and analyze it to give insights about the business and its operations. Based on the results, the business can use corrective measures to boost and improve its operational tactics.
As the need for data mining increased in business, the techniques that data mining offers also increased. Now, many data mining techniques are available according to different business and their data.
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As analysts and engineers say, “Use the right tool for the right job,” let’s dive deeper into details about the various data mining functionalities.
Data Mining Techniques
Data Warehousing
One of the most crucial data mining techniques is data warehousing.
Data warehousing is the technique of collecting and storing data. All the data collected by the data miners are stored in the data warehouse. The collected data is cleaned, filtered into a more usable form, and then uploaded into the warehouse. This data will be further used in the mining process.
Example: Data warehousing allows businesses to gather data from all their operation at one point, such as sales numbers, branch details, research reports, etc.
Data Cleaning
As the name hints, Data cleaning is a technique in which the raw data is analyzed, cleaned, and transformed into an optimal form for further analysis and processing. While cleaning the data, the analyst evaluates the data and identifies and removes errors, missing or duplicate data.
Data cleaning is the most important process of any analytical procedure. Working with inaccurate and wrong data is not only a waste of time but also a waste of resources and capital. And at the end, after all the resources and capital is utilized, the results are also faulty.
Example: Few examples of data cleaning are correcting syntax errors, incorrect spellings, missing values, etc.
Outlier Detection
Outlier detection is the most unique technique in the list of data mining techniques.
Outlier detection evaluates the database to find unique, diverse, and varying data. It finds errors, incorrect data, and missing data from the dataset. The outlier detects unique and varied data as they usually have been inputted incorrectly or with some error, making it easier to find incorrect data.
Example: In a sample of 50 people with similar weights, when the weight of 2 people is significantly heavy, it would be considered a univariate outlier.
Regression Analysis
Another one of the data mining techniques is the regression analysis.
Regression analysis helps to determine the relationship between variables. It helps the analyst to understand the relation between independent and dependent variables, i.e., how the changes in the independent variable affect the dependent variable. The main function of regression analysis is that it assists in forecasting.
Example: Regression helps businesses to analyze market behaviors, business trends and patterns, and forecasting.
Association Rule
The Association rule learning searches for a relationship among the variables in the dataset. The association rule is also known as the market basket analysis. The association rules help to find hidden links and patterns in the data set.
Example: Suppose you have been buying milk products for the last few months; a data mining association will examine the data and suggest products most frequently purchased with milk. Several online shopping apps, such as Amazon and Flipkart, use this technique.
Data Clustering
Another one on the list of data mining techniques is clustering.
In this technique, variables with similar attributes are grouped together into a cluster. This allows the analyst to divide the data more efficiently and take more effective data based on the cluster’s attributes.
Example: A supermarket using a clustering technique would analyze the sales of daily use products such as soap, milk, groceries, etc., analyze their previous sales and trends, and find patterns to predict future sales.
Data Classification
Next of the data mining techniques is the classification technique.
In the classification technique, data are assigned to different categories with similar characteristics. The goal of classifying the data into categories is to divide them into smaller groups, making it easier for evaluation and prediction. The classification is based on data sources, databases, and mining techniques.
Example: Supermarkets using this technique classify products such as electronics, groceries, clothes, etc., and evaluate the categories to find trends and patterns and forecast future sales.
Machine Learning
Machine learning is one of the most advanced techniques in the list of data mining techniques.
Machine learning is the process where the machine mimics human intelligence and performs analytical tasks such as learning, reasoning, problem-solving, etc. Machine learning offers a more accurate and reliable outcome because of its machine computation and human-like attributes. Machine learning has proven to help analyze and solve problems related to semi-structured and unstructured data.
Example: Machines can recognize speech, the person’s voice, and the words being said and process them accordingly.
Data Visualization
The last of the 8 data mining techniques is data visualization.
Data visualization is a technique in which the data is represented in a graphical form. Data visualization offers various graphic formats such as graphs, maps, charts, etc. The visual form helps analysts present and explain their findings more effectively.
Data visualization helps to represent complex data in graphical form to make it easy to understand. When presented in an easy and understandable format, it is easier to recognize areas that need more attention.
Example: A heat map is a visual representation of data in cold and hot form, which is explained using different color schemes. The warmer areas indicate that there is more interaction in that area.
ALSO READ : 13 Important Types of Data Visualization Tools That are used in Business
FAQs
What are the nine data mining techniques?
Data Warehousing
Data Cleaning
Outlier Detection
Regression Analysis
Association Rule
Data Clustering
Data Classification
Machine Learning
Data Visualization
What is the most popular data mining technique?
Association Rule
Data Clustering
Data Classification
Machine Learning
Outlier Detector
Closing Statement
Data mining is a handy and powerful tool designed for research purposes. Business organizations use this research tool to evaluate their firms and gather insights about business operations.
Data mining allows businesses to evaluate their current position based on past performance and current numbers. Data mining techniques also help businesses to use the data and forecast the business’s future.
As numerous amounts of data are available in various forms, data mining offers multiple techniques to counter this problem. Each technique has its unique purpose, which ultimately aids in data mining.
I hope this article about data mining techniques is informative. If you have any doubts or suggestions, upload the data in the comment box.