Is Data Mining And Machine Learning Same

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What Is The Difference Between Data Mining And Machine ...

What Is The Difference Between Data Mining And Machine …

What Is The Difference Between Data Mining And Machine Learning? | Bernard Marr
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Bernard Marr is a world-renowned futurist, influencer and thought leader in the fields of business and technology, with a passion for using technology for the good of humanity. He is a best-selling author of 20 books, writes a regular column for Forbes and advises and coaches many of the world’s best-known organisations. He has over 2 million social media followers, 1 million newsletter subscribers and was ranked by LinkedIn as one of the top 5 business influencers in the world and the No 1 influencer in the UK.
Bernard’s latest book is ‘Business Trends in Practice: The 25+ Trends That Are Redefining Organisations’
The huge leaps in Big Data and analytics over the past few years has meant that the average business user is now grappling with a whole new lexicon of tech-terminology. This can breed confusion, as people aren’t sure of the difference between terms and approaches. In my experience, ‘data mining’ and ‘machine learning’ are a prime example of this.
In this article, I define both data mining and machine learning, and set out how the two approaches differ. So if you’ve never quite grasped the difference, this article is for you.
What is data mining?
Data mining is a subset of business analytics and refers to exploring an existing large dataset to unearth previously unknown patterns, relationships and anomalies that are present in the data. It gives us the ability to find completely new insights that we weren’t necessarily looking for – unknown unknowns, if you like.
For example, if a business has a lot of data on customer churn, it could apply a data mining algorithm to find unknown patterns in the data and identify new associations that could indicate customer churn in the future. In this way, data mining is frequently used in retail to spot patterns and trends.
What is machine learning?
Machine learning is a subset of artificial intelligence (AI). With machine learning, computers analyse large data sets and then ‘learn’ patterns that will help it make predictions about new data sets. Apart from the initial programming and maybe some fine-tuning, the computer doesn’t need human interaction to learn from the data.
Put simply, machine learning is about teaching computers to learn a bit like humans do, by interpreting information and learning from our successes and failures. As an analytic process, it’s particularly useful for predicting outcomes. So, Netflix predicting you may want to watch Ozark next, based on the viewing preferences of other users with similar profiles, is an example of machine learning in action. Real-time fraud detection on credit card transactions is another example.
Why do people confuse the two?
As you can see, there are some similarities between the two concepts:
Both are analytics processes
Both are good at pattern recognition
Both are about learning from data so that we can improve decision making
Both require large amounts of data to be accurate
In fact, machine learning may use some data mining techniques to build models and find patterns, so that it can make better predictions. And data mining can sometimes use machine learning techniques to produce more accurate analysis.
What are the key differences?
Data mining and machine learning may, at heart, both be about learning from data and making better decisions. But the way they go about this is different. Here are some of the key differences between the two:
While data mining is simply looking for patterns that already exist in the data, machine learning goes beyond what’s happened in the past to predict future outcomes based on the pre-existing data.
In data mining, the ‘rules’ or patterns are unknown at the start of the process. Whereas, with machine learning, the machine is usually given some rules or variables to understand the data and learn.
Data mining is a more manual process that relies on human intervention and decision making. But, with machine learning, once the initial rules are in place, the process of extracting information and ‘learning’ and refining is automatic, and takes place without human intervention. In other words, the machine becomes more intelligent by itself.
Data mining is used on an existing dataset (like a data warehouse) to find patterns. Machine learning, on the other hand, is trained on a ‘training’ data set, which teaches the computer how to make sense of data, and then to make predictions about new data sets.
Clearly, there are some distinct differences between the two. Yet, as businesses look to become more and more predictive, we may see more overlap between machine learning and data mining in future. For example, more businesses may seek to improve their data mining analytics with machine learning algorithms.
Where to go from here
If you would like to know more about Machine Learning, AI and Big Data, cheque out my articles on:
What Is Machine Learning – A Complete Beginner’s Guide
What Is The Difference Between Artificial Intelligence And Machine Learning?
What Are Artificial Neural Networks – A Simple Explanation For Absolutely Anyone
What is Deep Learning AI? A Simple Guide With 8 Practical Examples
Or browse other related articles.
How Artificial Intelligence Can Help Small Businesses
Small and medium-sized businesses all over the world are benefiting from artificial intelligence and machine learning – and integrating AI into core business functions and processes is getting more accessible and more affordable every day. [… ]
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Data Mining vs Machine Learning - Javatpoint

Data Mining vs Machine Learning – Javatpoint

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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:
Unsupervised Learning
Supervised Learning
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
Factors
Data Mining
Machine Learning
Origin
Traditional databases with unstructured data.
It has an existing algorithm and data.
Meaning
Extracting information from a huge amount of data.
Introduce new Information from data as well as previous experience.
History
In 1930, it was known as knowledge discovery in databases(KDD).
The first program, i. e., Samuel’s checker playing program, was established in 1950.
Responsibility
Data Mining is used to obtain the rules from the existing data.
Machine learning teaches the computer, how to learn and comprehend the rules.
Abstraction
Data mining abstract from the data warehouse.
Machine learning reads machine.
Applications
In compare to machine learning, data mining can produce outcomes on the lesser volume of data. It is also used in cluster analysis.
It needs a large amount of data to obtain accurate results. It has various applications, used in web search, spam filter, credit scoring, computer design, etc.
Nature
It involves human interference more towards the manual.
It is automated, once designed and implemented, there is no need for human effort.
Techniques involve
Data mining is more of research using a technique like a machine learning.
It is a self-learned and train system to do the task precisely.
Scope
Applied in the limited fields.
It can be used in a vast area.
Next TopicFacebook Data Mining
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What is Machine Learning? A Definition. - Expert.ai

What is Machine Learning? A Definition. – Expert.ai

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
But, using the classic algorithms of machine learning, text is considered as a sequence of keywords; instead, an approach based on semantic analysis mimics the human ability to understand the meaning of a text.
Some Machine Learning Methods
Machine learning algorithms are often categorized as supervised or unsupervised.
Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.
In contrast, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources.
Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.
Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.
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Originally published March 2017, updated May 2020

Frequently Asked Questions about is data mining and machine learning same

Does data mining use machine learning?

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.

What is meant by machine learning in data mining?

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.May 6, 2020

Which one is better for data science machine learning or data mining?

Data Mining produces accurate results which are used by machine learning and thereby makes machine learning produce better results. As data mining requires human intervention, it may miss important relationships. Machine learning algorithms are proved to be more accurate than the Data Mining techniques.Aug 27, 2021

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