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Difference between Data Mining and Machine Learning
Data Mining and Machine Learning are two fields which have influenced each other. Data mining is the field in which operations are performed on sets of data to determine certain patterns in the data sets, whereas machine learning uses certain algorithms that automatically improves the analysis processes through data based experiences. Although data mining and machine learning have many common things, they are quite different from each other.
Read this article to learn more about Data Mining and Machine Learning and how they are different from each other.
What is Data Mining?
Data Mining is the process of discovering meaningful new correlations, patterns, and trends by sifting through a large amount of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques. It is the analysis of observational datasets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner.
It is the procedure of selection, exploration, and modeling of huge quantities of data to discover regularities or relations that are at first unknown to obtain clear and beneficial outcomes for the owner of the database. Data mining is the process of exploration and analysis by automatic or semi-automatic means of large quantities of data to discover meaningful patterns and rules.
Data Mining is similar to Data Science. It is carried out by a person, in a specific situation, on a particular data set, with an objective. This process includes various types of services such as text mining, web mining, audio and video mining, pictorial data mining, and social media mining. It is done through software that is simple or highly specific.
What is Machine Learning?
Machine learning primarily deals with the design and development of algorithms that can learn and improve on their own to make predictions on data. Machine learning algorithms use complex programs that can understand through experience, find patterns in data, and create predictions based on those patterns. Machine learning algorithms are used in data mining to automatically identify patterns in large data sets.
Machine learning algorithms improve themselves by frequent input of training information. The main objective of machine learning is to learn data and build models from data that can be understood and used by humans.
Types of Machine Learning
There are two types of machine learning which are as follows −
Unsupervised Machine Learning − Unsupervised learning does not base on trained data sets to forecast the results, but it uses direct techniques including clustering and related to predicting the results. Trained data sets are represented as the input for which the output is known.
Supervised Machine Learning − Supervised learning defines the presence of a supervisor as a teacher. Supervised learning is a learning technique in which it can teach or train the machine using data that is well leveled implies that some information is already marked with the true responses. After that, the machine is supported with the new sets of records so that the supervised learning algorithm analyzes the training information and provides an accurate result from labeled data.
Difference between Data Mining and Machine Learning
The following table highlights all the key differences between data mining and machine learning −
S.No. |
Data Mining |
Machine Learning |
|---|---|---|
1. |
Data mining, also referred to as Knowledge Discovery in Data, is a technique to identify any anomalies, correlations, trends, or patterns among millions of records (particularly structured data) to glean insights that could be helpful for business decision making and might have been missed during traditional analysis. |
Machine learning is a technique that creates complex algorithms for large data processing and provides outcomes to its users. It uses complex programs that can understand through experience and create predictions. |
2. |
The main goal of data mining is to find facts or information that was previously ignored or not known using complicated mathematical algorithms. |
The aim of machine learning is to understand information and build models from data that can be understood and used by humans. |
3. |
Data mining uses the database, data warehouse server, data mining engine, and pattern assessment methods to obtain beneficial data. |
Machine learning uses neural networks, predictive models, and automated algorithms to create decisions. |
4. |
It can be used in limited fields. |
It can be used in a vast area. |
5. |
The purpose of data mining is to obtain data rules from the existing data |
The purpose of machine learning is to teach the computer systems that how to learn and improve from the experiences. |
6. |
Data mining was known as KDD, Knowledge Discovery in Databases in the year of 1930. |
The concept of machine learning was introduced in 1950 in the form of Samuel's checker playing program. |
Conclusion
The most important point that you should note here is that data mining uses traditional databases with unstructured data, whereas machine learning uses algorithms and existing data to train the computer systems.
Data mining is a process of discovering patterns and knowledge from data, while machine learning is a field of study that focuses on the development of algorithms that can learn from the data and make predictions on the data.
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