Data Mining vs Machine Learning
Data Mining relates to extracting information from a large quantity of data. Data mining is a technique of discovering different kinds of patterns that are inherited in the data set and which are precise, new, and useful data. Data Mining is working as a subset of business analytics and similar to experimental studies. Data Mining's origins are databases, statistics.
Machine learning includes an algorithm that automatically improves through data-based experience. Machine learning is a way to find a new algorithm from experience. Machine learning includes the study of an algorithm that can automatically extract the data. Machine learning utilizes data mining techniques and another learning algorithm to construct models of what is happening behind certain information so that it can predict future results.
Data Mining and Machine learning are areas that have been influenced by each other, although they have many common things, yet they have different ends.
Data Mining is performed on certain data sets by humans to find interesting patterns between the items in the data set. Data Mining uses techniques created by machine learning for predicting the results while machine learning is the capability of the computer to learn from a minded data set.
Machine learning algorithms take the information that represents the relationship between items in data sets and creates models in order to predict future results. These models are nothing more than actions that will be taken by the machine to achieve a result.
What is Data Mining?
Data Mining is the method of extraction of data or previously unknown data patterns from huge sets of data. Hence as the word suggests, we 'Mine for specific data' from the large data set. Data mining is also called Knowledge Discovery Process, is a field of science that is used to determine the properties of the datasets. Gregory Piatetsky-Shapiro founded the term "Knowledge Discovery in Databases" (KDD) in 1989. The term "data mining" came in the database community in 1990. Huge sets of data collected from data warehouses or complex datasets such as time series, spatial, etc. are extracted in order to extract interesting correlations and patterns between the data items. For Machine Learning algorithms, the output of the data mining algorithm is often used as input.
What is Machine learning?
Machine learning is related to the development and designing of a machine that can learn itself from a specified set of data to obtain a desirable result without it being explicitly coded. Hence Machine learning implies 'a machine which learns on its own. Arthur Samuel invented the term Machine learning an American pioneer in the area of computer gaming and artificial intelligence in 1959. He said that "it gives computers the ability to learn without being explicitly programmed."
Machine learning is a technique that creates complex algorithms for large data processing and provides outcomes to its users. It utilizes complex programs that can learn through experience and make predictions.
The algorithms are enhanced by themselves by frequent input of training data. The aim of machine learning is to understand information and build models from data that can be understood and used by humans.
Machine learning algorithms are divided into two types:
1. Unsupervised Machine Learning:
Unsupervised learning does not depend on trained data sets to predict the results, but it utilizes direct techniques such as clustering and association in order to predict the results. Trained data sets are defined as the input for which the output is known.
2. Supervised Machine Learning:
As the name implies, supervised learning refers to the presence of a supervisor as a teacher. Supervised learning is a learning process in which we teach or train the machine using data which is well leveled implies that some data is already marked with the correct responses. After that, the machine is provided with the new sets of data so that the supervised learning algorithm analyzes the training data and gives an accurate result from labeled data.
Major Difference between Data mining and Machine learning
1. Two-component is used to introduce data mining techniques first one is the database, and the second one is machine learning. The database provides data management techniques, while machine learning provides methods for data analysis. But to introduce machine learning methods, it used algorithms.
2. Data Mining utilizes more data to obtain helpful information, and that specific data will help to predict some future results. For example, In a marketing company that utilizes last year's data to predict the sale, but machine learning does not depend much on data. It uses algorithms. Many transportation companies such as OLA, UBER machine learning techniques to calculate ETA (Estimated Time of Arrival) for rides is based on this technique.
3. Data mining is not capable of self-learning. It follows the guidelines that are predefined. It will provide the answer to a specific problem, but machine learning algorithms are self-defined and can alter their rules according to the situation, and find out the solution for a specific problem and resolves it in its way.
4. The main and most important difference between data mining and machine learning is that without the involvement of humans, data mining can't work, but in the case of machine learning human effort only involves at the time when the algorithm is defined after that it will conclude everything on its own. Once it implemented, we can use it forever, but this is not possible in the case of data mining.
5. As machine learning is an automated process, the result produces by machine learning will be more precise as compared to data mining.
6. Data mining utilizes the database, data warehouse server, data mining engine, and pattern assessment techniques to obtain useful information, whereas machine learning utilizes neural networks, predictive models, and automated algorithms to make the decisions.
Data Mining Vs Machine Learning