The concepts of data mining and machine learning are similar and therefore are often used interchangeably. Both analyze datasets to make predictions and gain insights. However, they are based on different principles. In ML, analysis is preceded by setting criteria for data categorization. Since this step foregoes data clearance, it allows the dismissal of unsuitable data from analysis. In DM, patterns are not known beforehand and have to be established.
Data mining uses algorithms to discover correlations and interdependencies in data and decipher their meaning, for example, customer preferences. One example is the discovery of periodic orders of pet food or shampoo to remind customers and encourage them to buy from the company.
Take another example. When a trading company wants to place an order for production based on past sales, it needs to find the best combination of items taking into account several factors.
The order should:
satisfy the increasing demand for the best sellers;
predict the optimal production of new items;
take into account seasonal fluctuations;
compensate for the lack of out-of-stock units;
replace certain SKUs with similar goods;
optimize the stock so it remains within the available space and the agreed cash flow.
Mathematical methods can only solve part of the problem, while data mining can provide a better solution.
Machine learning is a subset of AI and is about designing algorithms that learn from data and improve with experience. A spam filter is a common example of machine learning. Algorithms analyze each email and look for patterns that indicate whether it is spam or not (e.g., containing the words “free money” or coming from a suspicious domain). Machine learning algorithms are often used in e-commerce platforms and streaming services like Amazon and Netflix to make product recommendations. They analyze customers’ previous purchases and search history to determine what they might be interested in buying next.
Machine learning algorithms can be used for clustering, classification, regression (predictive analysis), association rule development, and anomaly detection, meaning they are more universal in their application, helping find out general trends and patterns.
Data mining methods are used to work with customer data and recognize similarities in a particular segment. So, depending on the task, you can use ML or data mining. In many cases, they complement and enrich each other. For example, data mining can help establish hypotheses that will subsequently be used for machine learning. Also, ML techniques can be used to verify these hypotheses.
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