Data Mining vs. Machine Learning: Key Differences You Should Know

The massive outbreak in the generation of data has propelled advancements in the fields of machine learning and artificial intelligence. Although data mining has been around for a longer period of time, there’s been a lot of confusion between fields that deals with understanding data. This post discusses the purpose and difference between machine learning and data mining. The following topics are covered:

1. What is data mining?
2. What is machine learning?
3. Differences between data mining and machine learning

What Is Data Mining?

Data mining involves extracting patterns and correlations from data.

Data fed to an algorithm is subjected to a set of statistical techniques to build a model. The model refines the data and draws significant patterns that, for instance, can help to cluster the data into distinct groups (called a clustering task). This process is termed “data mining.” As the name suggests, it mines and extracts useful patterns from data.

Data mining deals with unsupervised learning tasks, wherein the data fed to the algorithm only consists of input data and doesn’t contain information about the output. The process of data mining focuses on extracting the most relevant patterns from the unsupervised dataset.

What Is Machine Learning?

Broadly speaking, machine learning is a subset of artificial intelligence that allows a machine to learn and improve with experience. It also has a subfield called deep learning that uses the concept of artificial neural networks.

Machine learning is the process of using raw input data to make predictions. The input data goes through a series of steps in order for the machine to be able to use it for decision-making or predictions.

A machine learning task begins with gathering the input data and prepping it. Data preparation and preprocessing involves cleaning the data and dealing with missing values and redundant information. Furthermore, processes such as dimensionality reduction are also a part of preprocessing. Next, the data is fed into an algorithm that draws important insights and patterns from the data. As we’ve already discussed, this process is data mining.

After extracting the relevant patterns, these patterns go through analysis and interpretation. These insights are useful for making predictions on testing data. This is how a machine (a machine learning model, to be more precise) learns.

Moreover, if the accuracy of the prediction is low, the machine learning model can be tuned by adjusting various parameters or by using different algorithms (or an ensemble of algorithms). In this way, you can improve the accuracy of the machine learning model with experience.

Data Mining vs. Machine Learning

The purpose:

• The purpose of data mining is to derive useful insights and patterns from data. It doesn’t involve data preparation, preprocessing, dimensionality reduction, testing, and validation.
• Machine learning, on the other hand, involves multiple steps, as mentioned above. It uses data mining, analysis, and interpretation to draw relevant patterns from data in order to make intelligent decisions.

The data:

• Data mining is concerned with humongous datasets that are unsupervised in nature. It uses statistical techniques and methods to understand and draw important correlations between various variables.
• Machine learning isn’t limited to unsupervised tasks. It also performs supervised tasks that use datasets containing teacher outputs to supervise the machine learning model to make predictions.

The output:

• Data mining produces patterns that identify certain types of tasks, such as anomaly detection, clustering, or classification. Thus, data mining can identify similar groups or anomalies within the data.
• Machine learning produces a learning algorithm or a model that can predict the output, given the input. In addition to that, the model is capable of improving by feeding it more data or tweaking its parameters.

In Conclusion

Both data mining and machine learning have a wide range of applications. Fraud detection, market basket analysis, customer segmentation, and so on make use of data mining. Whereas machine learning encompasses many applications such as customer acquisition, social media, virtual assistants, self-driving cars, language translation, and much more.

The differences clearly show that there’s an overlap between machine learning and data mining. This is because they’re both techniques or methods used to study and understand data. While data mining is a useful tool for understanding data, machine learning, in addition to understanding data, is also capable of decision-making and predictions.

With this, we come to the end of this post, which we hope helped you understand the purpose and differences between machine learning and data mining.