Data Mining and it’s techniques
Learn how data mining can help us forecast our actions.
The procedure through which a large set of data is sorted to find out valuable information and patterns that can be modified into a coherent structure for further use is called Data Mining. The term data mining is a misnomer. It does not mean the extraction of data but the extraction of knowledge from the bulk of data or pattern analysis by using different types of Data Mining techniques. In simple terms, it involves data analysis and forecasts. Data mining is also called Knowledge Discovery in Databases(KDD).
The valuable information generated, can be used in business intelligence and advanced analytics applications that involve analysis of historical data, as well as real-time analytics applications that examine streaming data as it’s created or collected. It involves using techniques and technologies such as database management, statistics, and machine learning. There are different types of data techniques that can be put to use for mining knowledge effectively.
Data mining techniques are widely used in stores. Customers can get free loyalty cards from several supermarkets, which provide them access to exclusive discounts not available to non-members and these cards allow retailers to keep track of — who is purchasing what, when they are purchasing it, and at what price. After evaluating the data, retailers can use it to offer customers coupons based on their purchasing behaviour and determine when to put things on sale or sell them at full price.
What are Data mining Techniques?
Classification
Using classification techniques, relevant information from data can be obtained and classified into different categories. The enterprise can classify the related data using this data mining technique.
Clustering
Similar to classification, clustering involves dividing information into groups based on the correlations between them. It identifies similar data and clusters it together. This clustered data then helps to spot the differences and correlations between data.
Association
Another data mining technique that is related to tracking patterns but is more specific to dependently linked variables is Association. Highly similar specific events or characteristics are matched with similar events and characteristics.
Prediction
One of the most important data mining techniques that are used to predict the types of data the consumer might witness in the future is the Prediction technique. Having an understanding of the previous trends and recognizing them is enough to get a fairly accurate prediction of what the future holds.
Tracking Patterns
The most basic of all the data mining techniques is learning to identify patterns in the data sets. Generally, it singles out a few of the deviations in the data that occur at systematic intervals or an ebb and flow of a certain variable over time.
Regression.
The regression technique is the data mining technique that is essentially a form of planning and modelling that is used to recognize the probability of a certain variable, given the presence of other variables. It identifies and analyses the relationship between variables.
Conclusion
Data mining is a powerful tool that helps firms to focus on the most relevant information in their data warehouses by extracting hidden predictive information from massive databases. Praxis offers a 9-month Full-Time Post Graduate Program in Data Science and Post Graduate Program in Data Engineering that will help you master data mining and its techniques.
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