Both technologies also help professionals find answers to myriad problems; they provide a way to gain a deeper understanding that isn’t possible by simply looking at all of the disparate information and siloed datasets out there.
To provide a clear understanding of the differences between the two, let’s take a look at what drives each process.
Definition of 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.
Think of data mining as a search for information. It could be about people, concepts, behavior, or the devices people use for personal or business use. Data mining searches through vast amounts of data from different sources.
Data mining tools search for meaning in all this information. Data mining goes deeper than the human mind can go, finding patterns in seemingly unrelated data and putting it together to predict future outcomes.
Definition of Machine Learning
Machine learning is a branch of artificial intelligence devoted to guiding robots in their understanding of human behavior. Scientists and engineers hope machine learning will eventually help machines make unguided choices by independently interpreting input from the world around them.
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.
Scientists continue to explore another aspect of machine learning, known as deep learning, modeled after the workings of the brain itself. They hope to see automation anticipating behavior on its own, freeing it from the need to be fed information at all.
Key Differences Between Data Mining and Machine Learning
Data mining isn’t a new invention that came with the digital age. The concept has been around for over a century but came into greater public focus in the 1930s. According to hacker bits, one of the first modern moments of data mining occurred in 1936, when Alan Turing introduced the idea of a universal machine that can perform computations similar to those of modern-day computer. Forbes also reported on Turing’s development of the “Turing Test” in 1950 to determine if a computer has real intelligence or not. To pass his test, a computer needed to fool a human into believing it was also human. Just two years later, Arthur Samuel created The Samuel Checkers playing program that appears to be the world’s first self-learning program. It miraculously learned as it played and got better at winning by studying the best moves.
We have come a long way since then. Businesses are now harnessing data mining and machine learning to improve everything from their sale processes to interpreting financials for investment purposes.
Machine learning and data mining are two separate entities but are in harmony with each other. That is the reason why people use data mining and machine learning interchangeably. But, it’s imperative to understand there is a wide difference between them both.
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. |
DATA USE
One key difference between machine learning and data mining is how they are used applied in our everyday lives. For example, data mining is often used by machine learning to see the connections between relationships. Uber uses machine learning to calculate ETAs for rides or meal delivery times for UberEATS.
Data mining can be used for a variety of purposes, including financial research. Investors might use data mining and web scraping to look at a start-up’s financials and help determine if they want to offer to fund. A company may also use data mining to help collect data on sales trends to better inform everything from marketing to inventory needs, as well as to secure new leads. Data mining can be used to comb through social media profiles, websites, and digital assets to compile information on a company’s ideal leads to start an outreach campaign. Using data mining can lead to 10000 leads in 10 minutes. With this much information, a data scientist can even predict future trends that will help a company prepare well for what customers may want in the months and years to come.
Machine learning embodies the principles of data mining, but can also make automatic correlations and learn from them to apply to new algorithms. It’s the technology behind self-driving cars that can quickly adjust to new conditions while driving. Machine learning also provides instant recommendations when a buyer purchases a product from Amazon. These algorithms and analytics are constantly meant to be improving, so the result will only get more accurate over time. Machine learning isn’t artificial intelligence, but the ability to learn and improve is still an impressive feat.
Banks are already using and investing in machine learning to help look for fraud when credit cards are swiped by a vendor. Citibank invested in global data science enterprise Feedzai to identify and eradicate financial fraud in real-time across online and in-person banking transactions. The technology helps to rapidly identify fraud and can help retailers protect their financial activity.
FOUNDATIONS FOR LEARNING
Both data mining and machine learning draw from the same foundation but in different ways. A data scientist uses data mining pulls from existing information to look for emerging patterns that can help shape our decision-making processes. The clothing brand Free People, for example, uses data mining to comb through millions of customer records to shape their look for the season. The data explores best-selling items, what was returned the most, and customer feedback to help sell more clothes and enhance product recommendations. This use of data analytics can lead to improved customer experience overall.
Machine learning, on the other hand, can actually learn from the existing data and provide the foundation necessary for a machine to teach itself. Zebra Medical Vision developed a machine learning algorithm to predict cardiovascular conditions and events that lead to the death of over 500,000 Americans each year.
Machine learning can look at patterns and learn from them to adapt behavior for future incidents, while data mining is typically used as an information source for machine learning to pull from. Although data scientists can set up data mining to automatically look for specific types of data and parameters, it doesn’t learn and apply knowledge on its own without human interaction. Data mining also can’t automatically see the relationship between existing pieces of data with the same depth that machine learning can.
PATTERN RECOGNITION
Collecting data is only part of the challenge; the other part is making sense of it all. The right software and tools are needed to be able to analyze and interpret the huge amounts of information data scientists collect and find recognizable patterns to act upon. Otherwise, the data would largely be unusable unless data scientists could devote their time to looking for these complexes, often subtle and seemingly random patterns on their own. And anyone even somewhat familiar with data science and data analytics knows this would be an arduous, time-consuming task.
Businesses could use data to shape their sales forecasting or determine what types of products their customers really want to buy. For example, Wal-Mart collects point of sales from over 3,000 stores for its data warehouse. Vendors can see this information and use it to identify buying patterns and guide their inventory predictions and processes for the future.
It’s true that data mining can reveal some patterns through classifications and sequence analysis. However, machine learning takes this concept a step further by using the same algorithms data mining uses to automatically learn from and adapt to the collected data. As malware becomes an increasingly pervasive problem, machine learning can look for patterns in how data in systems or the cloud is accessed. Machine learning also looks at patterns to identify which files are actually malware, with a high level of accuracy. All this is done without the need for constant monitoring by a human. If abnormal patterns are detected, an alert can be sent out so action can be taken to prevent the malware from spreading.
IMPROVED ACCURACY
Both data mining and machine learning can help improve the accuracy of the data collected. However, data mining and how it’s analyzed generally pertains to how the data is organized and collected. Data mining may include using extracting and scraping software to pull from thousands of resources and sift through data that researchers, data scientists, investors, and businesses use to look for patterns and relationships that help improve their bottom line.
One of the primary foundations of machine learning is data mining. Data mining can be used to extract more accurate data. This ultimately helps refine your machine learning to achieve better results. A person may miss the multiple connections and relationships between data, while machine learning technology can pinpoint all of these moving pieces to draw a highly accurate conclusion to help shape a machine’s behavior.
Machine learning can enhance relationship intelligence in CRM systems to help sales teams better understand their customers and make a connection with them. Combined with machine learning, a company’s CRM can analyze past actions that lead to a conversion or customer satisfaction feedback. It can also be used to learn how to predict which products and services will sell the best and how to shape marketing messages to those customers.
FUTURE OF DATA MINING AND MACHINE LEARNING
The future is bright for data science as the amount of data will only increase. By 2020, our accumulated digital universe of data will grow from 4.4 zettabytes to 44 zettabytes, as reported by Forbes. We’ll also create 1.7 megabytes of new information every second for every human being on the planet.
As we amass more data, the demand for advanced data mining and machine learning techniques will force the industry to evolve in order to keep up. We’ll likely see more overlap between data mining and machine learning as the two intersect to enhance the collection and usability of large amounts of data for analytics purposes.
We’re just scratching the surface of what machine learning can do and how it will spread to help scale our analytical abilities and improve our technology. According to reporting from Geek wire, as our billions of machines become connected, everything from hospitals to factories to highways can be improved with IoT technology that can learn from other machines.
But some experts have a different idea about data mining and machine learning altogether. Instead of focusing on their differences, you could argue that they both concern themselves with the same question: “How we can learn from data?” At the end of the day, how we acquire and learn from data is really the foundation for emerging technology. It’s an exciting time not just for data scientists but for everyone that uses data in some form.
Source: Labvault & EduGrad
