As technology continues to advance and expand, a whole new range of technical terms and concepts are born from time to time. With the advent of Big Data and Data Science, today, we have Artificial Intelligence, Machine Learning, and Deep Learning. Since these new technologies are all inter-related and connected, people often tend to technological terms interchangeably. Two such terms are “Data Mining” and “Machine Learning.”
The Data Mining vs Machine Learning debate has been doing the rounds for quite a while now. Although both these Data Science concepts have been around us since the 1930s, they’ve only recently come to the fore. Oftentimes, people tend to blur the lines of difference between Data Mining and Machine Learning due to the presence of certain similar characteristics between the two. However, both are inherently different, and that’s what we wish to bring to light in this post – the difference between Data Mining and Machine Learning.
What is Data Mining?
Data Mining refers to the process of discovering meaningful patterns in large and complex datasets through a combination of multiple disciplines and tools, including Computer Science, Machine Learning, Statistics, and database systems. Data Mining is a subset of Machine Learning that centres around exploratory data analysis through unsupervised learning.
The end goal of Data Mining is to extract relevant information (and not the “extraction” of raw data itself) from datasets and transform the same into business-savvy insights for further use. If you are a beginner and interested to learn more about data science, check out our data science certification from top universities.
What is Machine Learning?
Machine Learning is a sub-branch of Artificial Intelligence. It is the scientific study of intelligent algorithms and statistical models that can be used by machines (computers) to perform human-like tasks without being explicitly programmed or trained for it. A unique aspect of Machine Learning algorithms is that they can learn through experience.
Data Mining vs Machine Learning: Key Differences
Both Data Mining and Machine Learning are sub-domains of Data Science. So, naturally, they are inter-related. Data Mining is, in fact, a crucial part of Machine Learning, and it is used to find valuable patterns and trends hidden within vast volumes of data.
Data Mining and Machine Learning both employ advanced algorithms to uncover relevant data patterns. However, even though Data Mining and Machine Learning intersect each other, they have a fair share of differences as to how they are used.
Let’s look at some of the core differences between Data Mining and Machine Learning.
1. Use of data
The principal difference between Data Mining and Machine Learning lies in how each uses data and applies it to various applications. While Data Mining relies on vast repositories of Big Data from which it extracts meaningful patterns, Machine Learning works primarily with algorithms instead of raw data.
Data Mining is used for many different purposes. For instance, BFSI companies can use it for financial research, whereas an e-commerce company can use it for mining sales data to identify the key trends in the present market. Data Mining can also be used for combing through websites, social media profiles, and even digital assets for obtaining insights on a brand’s or company’s potential leads – it can help generate 10,000 leads within 10 minutes!
On the contrary, although Machine Learning incorporates the principles of Data Mining, it seeks to establish automatic correlations to learn from them and apply the findings to new ML algorithms. Since ML algorithms are programmed to learn from experience, they are continually improving, thereby delivering more accurate results over time.
2. Learning foundation
Although Data Mining and Machine Learning learn from the same foundation, their approach is different.
Data Mining draws from existing information to identify emerging patterns that can shape the decision-making processes of a business. Free People, a clothing brand uses Data Mining to browse through massive volumes of existing customer records to create personalized product recommendations for individual customers.
Machine Learning, however, can “learn” from the existing data and create an ideal learning foundation for the machine to teach itself. While Machine Learning looks at patterns and learns from them to predict trends for future incidents, Data Mining functions as an information source for Machine Learning to pull from.
Unlike Data Mining, Machine Learning can automatically identify the relationship between existing pieces of data.
3. Recognizing the patterns within data
Once the data is collected, the real challenge lies in making sense of it – the analysis and interpretation part are pivotal to transforming raw data into ready-to-use insights for business. This is where Data Scientists and Data Analysts have to decide on which software and tool to use to analyze and interpret large volumes of unstructured data and find the recognizable patterns within it. Read about the most used data science tools in 2020.
If you skip this step, the data at your disposal is of no use at all. Data Mining can reveal some useful patterns through classification and sequence analysis, whereas, Machine Learning can crank this up a notch by using the same algorithms used by Data Mining to learn from and adapt to the gathered data automatically. This is why Machine Learning is now increasingly used for malware detection.
According to Deep Instinct, an institutional intelligence company, each piece of new malware retains almost the same code as the older versions, and that only 2-10% of the malware files change from iteration to iteration. Deep Instinct’s ML model can predict which files in a system are malware files with great accuracy, despite the 2–10% variations.
Data Mining and Machine Learning are both used to enhance and improve the accuracy of the accumulated data. However, Data Mining and its analysis are limited to how the data is organized and collected. Data Mining acts as a means to extract relevant insights from complex datasets to improve the predictive capabilities of ML algorithms and models.
As we mentioned earlier, Data Mining may miss out on multiple connections and relationships between the data at hand, but ML does not – it can identify the correlations between all relevant data points to deliver highly accurate conclusions and ultimately shape the model’s behaviour.
For instance, Machine Learning is now used in CRM systems to enhance their relationship intelligence, thereby allowing a company’s sales team to understand their customers better. ML-powered CRM systems can analyze past actions to boost conversions and also improve customer satisfaction scores. Furthermore, Machine Learning can train CRM systems to accurately predict which products/services will sell the best and when, and to what customer segments.
Data Mining vs Machine Learning: The Future
According to recent estimates surrounding Big Data, by this year, that is, by 2020, every human being on the planet will generate around 1.7 megabytes of new information every second. Consequently, global data will grow from 4.4 zettabytes to 44 zettabytes!
As more and more data continues to amass every second, the demand for Data Science tools like Data Mining, Machine Learning, and Artificial Intelligence is bound to increase with time. Get to know more about applications of machine learning.
All companies, organizations, and institutions using Big Data will continue to create the need for advanced technologies like Data Mining and Machine Learning to gather data and analyze and interpret it for business purposes. Naturally, the future of both these emerging technologies is highly promising.
In the August 2004 issue of DM Review, Lou Agosta stated: “The future of data mining lies in predictive analytics.” One of the most significant applications of predictive analytics is in the field of Medical Research. Predictive analytics, or “one-click data mining” simplifies and automates the data mining process, thereby enabling researchers to apply advanced analytics across the entire spectrum life sciences, right from drug discovery through to marketing.
Right now, technologies like Machine Learning and Data Mining are still at their nascent stage, and much more is yet to come. As these technologies mature with time, new applications, use cases, and breakthroughs will emerge to transform our lives even further. Rest assured, despite their differences, Data Mining and Machine Learning will continue to function intricately to make sense of data.
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What is a CRM system used for?
Customer relationship management systems essentially assist any company in storing customer and prospect data in order to assess customer satisfaction and discuss it with other employees. All conversations, emails, and meetings are recorded and analyzed by a CRM system. It supports companies in streamlining procedures and client connections in order to boost sales, enhance customer service, and maximize profits.
Who gets paid more—a machine learning engineer or a data scientist?
Machine learning engineers make somewhat more than data scientists, but when we consider the quantity of job openings, data science comes out on top. This is because machine learning engineers work in the field of artificial intelligence, which is a relatively young field. However, in order to earn a decent salary, one must ensure that the sector in which they work is one in which they have a strong interest. If you're more interested in machine learning, go for it; if you're more interested in data science, consider developing a career in that industry.
What are the responsibilities of a machine learning engineer?
A machine learning engineer's responsibilities vary based on the team, company, and industry in which they work. While a machine learning engineer's main role is to develop, implement, and maintain machine learning systems by combining data science and computer science basics, this can take many different shapes depending on the project type. They create machine learning systems, use ML algorithms to make correct predictions, and troubleshoot data set issues.