What is the Difference Between Data Mining Vs Machine Learning Vs Artificial Intelligence Vs Deep Learning Vs Data Science:
Both Data Mining and Machine learning are areas which have been inspired by each other, though they have many things in common, yet they have different ends.
Data mining is performed by humans on certain data sets with the aim to find out interesting patterns between the items in a data set. Data mining uses techniques developed by machine learning for predicting the outcome.
Whereas Machine Learning is the ability of a computer to learn from mined datasets.
The machine learning algorithms take the information representing the relationship between items in data sets and build models so that it can predict future outcomes. These models are nothing but actions which will be taken by the machine to get to a result.
This article will brief you all about Data Mining Vs Machine Learning in detail.
What You Will Learn:
- What is Data Mining?
- What is Machine Learning?
- Differences between Machine Learning vs Data Mining in Tabular Format
- What is Artificial Intelligence?
- Data Mining vs Machine Learning
- Data Mining, Machine Learning Vs Deep Learning
- Data Mining, Machine Learning Vs Data science
- Statistical Analysis
- Some Examples of Machine Learning
- Recommended Reading
What is Data Mining?
Data mining which is also known as Knowledge Discovery Process is a field of science that is used to find out the properties of datasets. Large sets of data collected from RDMS or data warehouses or complex datasets like time series, spatial, etc are mined to take out interesting correlations and patterns among the data items.
These results are used to improve business processes, and thereby result in gaining business insights.
Recommended Read => Top 15 Free Data Mining Tools
The term “Knowledge Discovery in Databases” (KDD) was coined by Gregory Piatetsky-Shapiro in 1989. The term “data mining” appeared in the database community in 1990.
What is Machine Learning?
Machine Learning is a technique which develops complex algorithms for processing large data and delivers results to its users. It uses complex programs which can learn through experience and make predictions.
The algorithms are improved by itself through regular input of training data. The goal of machine learning is to understand data and build models from data that can be understood and used by humans.
The term Machine Learning was coined by Arthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence in 1959 and he stated that “it gives computers the ability to learn without being explicitly programmed”.
Suggested Read => Most Popular Machine Learning Tools
Machine learning is classified into Two types:
- Unsupervised Learning
- Supervised Learning
Unsupervised Machine Learning
Unsupervised learning does not rely on trained data sets to predict the outcomes but it uses direct techniques such as clustering and association in order to predict outcomes. Trained data sets mean the input for which the output is known.
Supervised Machine Learning
Supervised Learning is like teacher-student learning. The relation between the input and the output variable is known. The machine learning algorithms will predict the outcome on the input data which will be compared with the expected outcome.
The error will be corrected and this step will be performed iteratively till an acceptable level of performance is achieved.
Differences between Machine Learning vs Data Mining in Tabular Format
|Factors||Data Mining||Machine Learning|
|1. Scope||Data Mining is used to find out how different attributes of a data set are related to each other through patterns and data visualization techniques.|
The goal of data mining is to find out relationship between 2 or more attributes of a dataset and use this to predict outcomes or actions.
|Machine Learning is used for making predictions of the outcome such as price estimate or time duration approximation.
It automatically learns the model with experience over time. It provides real time feedback
|2. Working||Data Mining is the technique of digging deep into data to take out useful information .||Machine Learning is method of improving complex algorithms to make machines near to perfect by iteratively feeding it with trained dataset.|
|3. Uses||Data Mining is more often used in research field such as web mining, text mining, fraud detection||Machine learning has more uses in making recommendations of products, prices, estimating the time required for delivery etc.|
|4. Concept||The concept behind mining is to extract information using techniques and find out the trends and patterns.||Machine Learning runs on the concept that machines learns from existing data and learns and improves by itself. Machine learning uses data mining methods and algorithms to build models on logic behind data which predict the future outcome. The algorithms are built on Math’s and programming languages|
|5. Method||Data mining will perform analysis in Batch format at a particular time to produce results rather than on continuous basis.||Machine Learning uses the data mining technique to improve its algorithms and change its behavior to future inputs. Thus data mining acts as an input source for machine learning.
Machine learning algorithms will continuously run and improve the performance of system automatically, also analyze when the failure can occur.
When there is some new data or change is trend, the machine will incorporate the changes without need to reprogram or human interference.
|6. Nature||Data mining requires human intervention for applying techniques to extract information.||Machine Learning is different from Data Mining as machine learning learns automatically.|
|7. Learning Capability||Data Mining requires the analysis to be initiated by human thus it is a manual technique.||Machine Learning is a step ahead of data mining as it uses the same techniques used by data mining to automatically learn and adapt to changes. It is more accurate then data mining.|
|8. Implementation||Data mining involves building models on which data mining techniques are applied. Models like CRISP-DM model are built.|
Data mining process uses database, data mining engine and pattern evaluation for knowledge discovery .
|Machine Learning is implemented by using Machine Learning algorithms in artificial intelligence, neural network, neuro fuzzy systems and decision tree etc.
Machine learning uses neural networks and automated algorithms to predict outcomes.
|9. Accuracy||Accuracy of data mining depends on how data is collected.|
Data Mining produces accurate results which are used by machine learning making machine learning produce better results.
Since data mining requires human intervention, it may miss important relationships
|Machine learning algorithms are proved to be more accurate than Data Mining techniques|
|10. Applications||Relative to machine learning, data mining can produce results on lesser volume of data.||Machine learning algorithm need data to be fed in standard format, due to which the algorithms available are limited.
To analyze data using machine learning, data from multiple sources should be moved from native format to standard format for the machine to understand.
Also it requires large amount of data for accurate results
|11. Examples||Places where data mining is used is in identifying sales patterns or trends, by cellular companies for customer retention and so on.||Machine learning is used in running marketing campaigns, for medical diagnosis, image recognition etc.|
What is Artificial Intelligence?
Artificial Intelligence is a branch of science which deals with the creation of intelligent machines. These machines are called intelligent as they have their own thinking and decision-making capabilities like human beings.
Examples of AI machines include Speech Recognition, Image Processing, Problem-solving, etc.
Also Read => List of the Top Artificial Intelligence Software
Artificial Intelligence, Machine learning, and Data mining are frequently used altogether in today’s world. These words are highly inter-related to each other and sometimes are used interchangeably.
So let us compare each of them in detail:
Artificial Intelligence and Data Mining
Artificial Intelligence is the study to create intelligent machines which can work like humans. It does not depend on learning or feedback, rather it has directly programmed control systems. The AI systems come up with the solutions to the problems on their own by calculations.
The data mining technique in mined data is used by the AI systems for creating solutions. Data mining serves as a foundation for artificial intelligence. Data mining is a part of programming codes with information and data necessary for AI systems.
Artificial Intelligence and Machine Learning
A large area of Artificial Intelligence is Machine Learning. By this, we mean that AI uses machine learning algorithms for its intelligent behavior. A computer is said to learn from some task if the error continuously decreases and if it matches the performance as desired.
Machine learning will study algorithms that will perform the task of extraction automatically. Machine learning comes from statistics but it is not actually. Similar to AI, machine learning also has a very broad scope.
Data Mining vs Machine Learning
Data mining and Machine Learning fall under the same world of Science. Though these terms are confused with each other, there are some major differences between them.
#1) Scope: Data Mining is used to find out how different attributes of a data set are related to each other through patterns and data visualization techniques. The goal of data mining is to find out the relationship between 2 or more attributes of a data set and use this to predict the outcomes or actions.
Machine Learning is used for making predictions of the outcome such as price estimate or time duration approximation. It automatically learns the model with experience over time. It provides real-time feedback.
#2) Function: Data Mining is the technique of digging deep into data to take out useful information. Whereas Machine Learning is a method of improving complex algorithms to make machines near to perfect by iteratively feeding it with the trained dataset.
#3) Uses: Data Mining is more often used in the research field while machine learning has more uses in making recommendations of the products, prices, time, etc.
#4) Concept: The concept behind data mining is to extract information using techniques and find out the trends and patterns.
Machine Learning runs on the concept that machines learn from the existing data and improves by itself. Machine learning uses data mining methods and algorithms to build models on the logic behind data which predict the future outcome. The algorithms are built on Maths and programming languages.
#5) Method: Machine Learning uses the data mining technique to improve its algorithms and change its behavior to future inputs. Thus data mining acts as an input source for machine learning.
Machine learning algorithms will continuously run and improve the performance of the system automatically, and also analyze when the failure can occur. When there is some new data or change in the trend, the machine will incorporate the changes without the need to reprogram or any human interference.
Data mining will perform analysis in the Batch format at a particular time to produce results rather than on a continuous basis.
#6) Nature: Machine Learning is different from Data Mining as machine learning learns automatically while data mining requires human intervention for applying techniques to extract information.
#7) Learning Capability: Machine Learning is a step ahead of data mining as it uses the same techniques used by data mining to automatically learn and adapt to changes. It is more accurate than data mining. Data Mining requires the analysis to be initiated by human and thus it is a manual technique.
#8) Implementation: Data mining involves building models on which data mining techniques are applied. Models like the CRISP-DM model are built. Data mining process uses a database, data mining engine and pattern evaluation for knowledge discovery.
Machine Learning is implemented by using Machine Learning algorithms in artificial intelligence, neural network, neuro-fuzzy systems, and decision tree, etc. Machine learning uses neural networks and automated algorithms to predict the outcomes.
#9) Accuracy: Accuracy of data mining depends on how data is collected. 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.
#10) Applications: Machine learning algorithm needs data to be fed in a standard format, due to which the algorithms available are much limited. To analyze data using machine learning, data from multiple sources should be moved from native format to standard format for the machine to understand.
It also requires a large amount of data for accurate results. This is an overhead when compared to data mining.
#11) Examples: Data mining is used in identifying sales patterns or trends while machine learning is used in running marketing campaigns.
Data Mining, Machine Learning Vs Deep Learning
Machine Learning comprises of the ability of the machine to learn from trained data set and predict the outcome automatically. It is a subset of artificial intelligence.
Deep Learning is a subset of machine learning. It works in the same way on the machine just like how the human brain processes information. Like a brain can identify the patterns by comparing it with previously memorized patterns, deep learning also uses this concept.
Deep learning can automatically find out the attributes from raw data while machine learning selects these features manually which further needs processing. It also employs artificial neural networks with many hidden layers, big data, and high computer resources.
Data Mining is a process of discovering hidden patterns and rules from the existing data. It uses relatively simple rules such as association, correlation rules for the decision-making process, etc. Deep Learning is used for complex problem processing such as voice recognition etc. It uses Artificial Neural Networks with many hidden layers for processing.
At times data mining also uses deep learning algorithms for processing the data.
Data Mining, Machine Learning Vs Data science
Data Science is a vast area under which Machine Learning comes. Many technologies such as SPARK, HADOOP, etc also come under data science. Data science is an extension of statistics which has the capability to process massively large data using technologies.
It deals with all real-world complex problem solving such as requirement analysis, understanding, extracting useful data, etc.
Data Science deals with human-generated raw data, it can analyze the images, and audios from data just like how humans do. Data science requires a high skill set with domain expertise, strong knowledge of databases, etc. It demands high computational resources, high RAM, etc.
Data Science models have clearly defined milestones to achieve when compared to Machine Learning which tries to achieve the target only with the available data.
Data Science Model comprises of:
- ETL- Extract Load and Transform data.
- Data Distribution and processing.
- Automated models application for outcomes.
- Data Visualization
- Reporting with slice and dice feature for better understanding.
- Data Backup, recovery and security.
- Migration to production.
- Running business models with the algorithms.
Statistics form the main part of data mining and machine learning algorithms. Statistical analysis uses numeric data and involves a lot of mathematical equations for inferencing the outputs.
It provides the right tools and techniques for analysis of large volume data. It covers a broad area of data analysis and covers the entire data lifecycle right from planning to analysis, presenting and creating reports.
There are two types of statistical analysis as mentioned below:
The descriptive analysis summarizes the data and inferential analysis uses the summarized data to draw results.
Statistics is applied in various fields i.e. in geography to determine the per capita population, in economics to study the demand and supply, in banking to estimate the deposits for a day and so on.
Some Examples of Machine Learning
Enlisted below are few examples of Machine Learning.
#1) Online Chat support by websites: The Bots used by several websites to provide instant customer service are powered by Artificial Intelligence.
#2) Email Messages: The email services automatically detect whether the content is spam or not. This technique is also powered by AI which looks at the attachments and content to determine if it’s suspicious or harmful for the computer user.
#3) Marketing Campaigns: Machine learning provides suggestions about a new product or similar products to its customers. Based on the customer choices, it will automatically frame deals instantly when the customer is live in order to attract him to buy. For example, lightning deals by Amazon.
Data becomes the most important factor behind machine learning, data mining, data science, and deep learning. The data analysis and insights are very crucial in today’s world. Hence investing time, effort, as well as costs on these analysis techniques, forms a critical decision for businesses.
As data is growing at a very fast pace, these methods should be fast enough to incorporate the new data sets and predict useful analysis. Machine learning can help us to quickly process the data and deliver quicker results in the form of models automatically.
Data mining techniques produce patterns and trends from historical data to predict future outcomes. These outcomes are in the form of graphs, charts, etc. Statistical analysis forms an integral part of data analysis and will grow higher in the near future.
These technologies will immensely grow in the future as business processes improve. These, in turn, will also help the businesses to automate the manual process, increase sales and profits, and thereby help in customer retention.
Hope you would have gained immense knowledge on Data Mining Vs Machine Learning!