In the eyes of most non-computer professionals and some computer professional backgrounds, Data Mining and Machine Learning are two deep areas. In my opinion, this is a habitual misunderstanding that is too high to "seek" (I have added a lot of attributives here). In fact, these two fields, like other areas of computers, are constantly proficient and in-depth in the process of integrating theory and practice. The only difference is that they infiltrate more mathematical knowledge (mainly statistics), in the latter In the article I will try to explain these mathematical knowledge to you in a way that is easier to understand. This article analyzes their relationships and similarities and differences from the basic concepts, not to mention specific algorithms and mathematical formulas. I hope to help everyone.
First, the concept definitionMachine Learning (ML) is a multidisciplinary subject involving many disciplines such as probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory. It specializes in how computers simulate or implement human learning behaviors to acquire new knowledge or skills, reorganize existing knowledge structures, and continually improve their performance.
Data mining is a non-trivial process of obtaining effective, novel, potentially useful, and ultimately understandable patterns from vast amounts of data. Data mining uses a large number of data analysis techniques provided by the machine learning community and data management technologies provided by the database community.
Learning ability is a very important feature of intelligent behavior. A system without learning ability can hardly be called a real intelligent system, while machine learning hopes that (computer) system can use experience to improve its performance, so the field It has always been one of the core research areas of artificial intelligence. In computer systems, "experience" usually exists in the form of data. Therefore, machine learning involves not only the exploration of human cognitive learning processes, but also the analysis and processing of data. In fact, machine learning has become one of the innovation sources of computer data analysis technology. Since almost all disciplines face data analysis tasks, machine learning has begun to affect many areas of computer science, and even many disciplines other than computer science. Machine learning is an important tool in data mining. However, data mining not only needs to research, expand, and apply some machine learning methods, but also solves practical problems such as data warehousing, large-scale data, and data noise through many non-machine learning technologies. Machine learning is also very broad, and the methods commonly used in data mining are usually just "learning from data." However, machine learning can not only be used in data mining, but some sub-fields of machine learning are not even related to data mining, such as enhanced learning and automatic control. Therefore, the author believes that data mining is from the perspective of purpose, machine learning is from the method, and there are considerable intersections between the two fields, but they cannot be equated.
Second, the relationship and differenceRelationship: Data mining can be thought of as the intersection of database technology and machine learning. It uses database technology to manage massive amounts of data and uses machine learning and statistical analysis for data analysis. The relationship is as follows:
Data mining has been affected by many subject areas, among which databases, machine learning, and statistics undoubtedly have the greatest impact. Roughly speaking, databases provide data management techniques, and machine learning and statistics provide data analysis techniques. Because the statistical community is often obsessed with the beauty of the theory and ignores the actual utility, many of the techniques provided by the statistical community are usually further studied in the machine learning community, and become effective machine learning algorithms before they can enter the data mining field. In this sense, statistics mainly affects data mining through machine learning, while machine learning and databases are the two supporting technologies for data mining.
The difference: data mining is not just a simple application of machine learning in industry. There are at least two important differences between them:
1. Traditional machine learning research does not treat massive data as a processing object. Therefore, data mining must perform special and non-simple transformation of these technologies and algorithms.
2. As an independent discipline, data mining also has its own unique things: correlation analysis. Simply put, the correlation analysis is to find out from the data that the “people who buy diapers are likely to buy beer†looks like an incredible but potentially meaningful model.
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