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The relationship between machine learning and data mining
In the eyes of many non-computer professionals, and even some computer experts, Data Mining and Machine Learning are often seen as two complex and deeply technical fields. In my view, this is a common misconception that overestimates their complexity. In reality, these two areas, like other computer science domains, become more familiar and profound through continuous practice and theoretical integration. The main distinction lies in the fact that they involve more mathematical concepts, particularly statistics. In this article, I aim to simplify and explain these mathematical ideas in an accessible way. I will explore their relationships, similarities, and differences from a conceptual perspective, without diving into specific algorithms or formulas. My goal is to provide clarity and help you better understand both fields.
**First, the concept definition**
Machine Learning (ML) is an interdisciplinary field that combines elements from probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory. It focuses on enabling computers to simulate or replicate human learning behaviors, thereby acquiring new knowledge, refining existing structures, and improving performance over time.
Data Mining, on the other hand, is the process of extracting useful, novel, and meaningful patterns from large volumes of data. It leverages techniques from machine learning and data management technologies to uncover insights that can be understood and applied effectively.
There's a strong link between learning ability and intelligent behavior. A system without the ability to learn is unlikely to be considered truly intelligent. Machine learning aims to improve system performance through experience, which is typically represented in the form of data. As a result, machine learning has been a central area of research in artificial intelligence. It not only explores how humans learn but also analyzes and processes data. Over time, it has become a key driver of innovation in data analysis. Since almost every field deals with data, machine learning now influences various areas of computer science and beyond. While it plays a crucial role in data mining, the latter also relies on other technologies such as data warehousing, handling large-scale data, and managing noise. Although many machine learning methods are used in data mining, not all subfields of machine learning are related to it—for example, reinforcement learning and control systems. Therefore, I believe that data mining is driven by purpose, while machine learning is about methods. They overlap significantly, but they are not the same.
**Second, the relationship and difference**
Relationship: Data mining can be viewed as the intersection of database technology and machine learning. It uses databases to manage large datasets and applies machine learning and statistical methods for analysis. This relationship is illustrated below:
[Image: The relationship between machine learning and data mining]
Data mining has been influenced by several disciplines, with databases, machine learning, and statistics having the most significant impact. Databases provide tools for data management, while machine learning and statistics offer analytical techniques. Often, statistical methods are refined within the machine learning community before being applied in data mining. In this sense, statistics influences data mining primarily through machine learning. Together, machine learning and databases serve as the backbone of data mining.
Difference: Data mining is more than just applying machine learning in real-world scenarios. There are at least two key differences:
1. Traditional machine learning does not typically deal with massive datasets. Therefore, data mining requires adapting and modifying these techniques to handle large-scale data efficiently.
2. As an independent discipline, data mining also includes unique aspects such as correlation analysis. For instance, finding that "people who buy diapers are likely to buy beer" may seem surprising, but it represents a potentially valuable insight that data mining helps uncover.