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The relationship between machine learning and data mining
In the eyes of many non-computer professionals, and even some in the field, Data Mining and Machine Learning are often seen as two complex and deeply technical areas. However, I believe this is a common misconception—something that people tend to overcomplicate or "overthink." In reality, both fields, like many others in computer science, become more familiar and advanced through the continuous integration of theory and practice. The main difference lies in the fact that they involve a greater amount of mathematical knowledge, especially statistics. In this article, I will try to explain these concepts in a more approachable way, without diving into specific algorithms or formulas. My goal is to clarify their relationship, similarities, and differences from a foundational perspective, hoping to provide a clearer understanding for everyone.
**First, the Concept Definition**
Machine Learning (ML) is an interdisciplinary field that draws from probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory. It focuses on how computers can simulate or replicate human learning behaviors in order to gain new knowledge, restructure existing knowledge, and continuously improve performance.
Data Mining, on the other hand, is a process of discovering effective, novel, potentially useful, and understandable patterns from large volumes of data. It leverages data analysis techniques from the machine learning community and data management technologies from the database community.
Learning ability is a key feature of intelligent behavior. A system without learning capabilities is hard to consider truly intelligent. Machine learning aims for systems to improve performance through experience, which is typically represented in the form of data. Therefore, machine learning not only explores human cognitive processes but also involves data analysis and processing. As a result, it has become a major source of innovation in data analysis technology. With almost every discipline facing data challenges, machine learning has influenced various areas of computer science and beyond. While it's a crucial tool in data mining, data mining also incorporates other non-machine learning technologies to handle issues like data warehousing, large-scale data, and noise. Although machine learning is broad, the methods used in data mining are usually just about “learning from data.†Moreover, some subfields of machine learning, such as reinforcement learning and control systems, are not related to data mining at all. Therefore, I believe that data mining is purpose-driven, while machine learning is method-oriented. Though they overlap significantly, they should not be considered 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 amounts of data and applies machine learning and statistical analysis for data exploration. This relationship is illustrated below:
[Image: The relationship between machine learning and data mining]
Data Mining has been influenced by many disciplines, with databases, machine learning, and statistics having the most significant impact. Databases provide data management techniques, while machine learning and statistics offer analytical tools. 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, while both machine learning and databases serve as core technologies for data mining.
**Difference:** Data Mining is not simply the application of machine learning in industry. There are at least two important distinctions:
1. Traditional machine learning research does not typically focus on handling massive datasets. As a result, data mining must adapt and modify these techniques and algorithms for large-scale data processing.
2. As an independent discipline, data mining has its own unique aspects, such as correlation analysis. For example, the well-known pattern "people who buy diapers are likely to buy beer" is a classic example of uncovering meaningful relationships within data.