Data Mining Vs. Machine Learning: All You Need to Know
Advanced technologies like data mining and machine learning have been in the spotlight for quite some time now. You would be surprised to know that data mining and machine learning is not a new invention that came into existence with the digital era. Hacker Bits cites that one of the first modern moments of data […]READ MORE >>
Advanced technologies like data mining and machine learning have been in the spotlight for quite some time now. You would be surprised to know that data mining and machine learning is not a new invention that came into existence with the digital era. Hacker Bits cites that one of the first modern moments of data mining occurred in 1936. It was during this time that Alan Turing formulated the idea of a universal machine that could perform computations that are similar to those of modern-day computers. A few years later Arthur Samuel, who coined the term ‘machine learning’, created The Samuel Checkers-playing program that is deemed to be the world’s first self-learning program. We have arguably come a long way since then; modern businesses are now utilizing data mining and machine learning applications to improve everything from their sales processes to interpreting financials for investment purposes. Data mining and machine learning are often interchanged or confused with each other. A significant similarity between the two is that both are rooted in data science. However, there are a few factors that differentiate these advanced technologies. Here is a look at what they are:
One of the key pointers of difference between data mining and machine learning applications is the way in which they are applicable to our everyday lives. Data mining is often used by machine learning applications to understand the correlations between different variables. For instance, Uber uses machine learning to calculate ETAs for rides or meal delivery times for UberEATS. But it’s not the only application that data mining is limited to. Businesses use it for financial research, to collect data on sales trends, and to secure new leads. It can also be used to analyze social media profiles, websites, and digital assets to compile information on a company’s ideal leads to start an outreach campaign. Machine learning, on the other hand, embodies the principles of data mining, but at the same time can make automatic correlations and learn from them to apply to new algorithms. Machine learning applications are already being put to a variety of uses such as fraud detection, providing instant recommendations based on purchase patterns, and even in self-driving cars.
Though both data mining and machine learning applications draw from the same foundation, they operate in different ways. Data mining pulls data from existing information to identify emerging patterns that can help shape the decision-making processes. For example, the clothing brand Free People uses data mining to comb through millions of customer records to develop their look for the season. It helps them to explore the best-selling items, the item that was returned the most, and customer feedback to help sell more clothes and enhance product recommendations. Machine learning studies the existing data and provides the foundation necessary for a machine to teach itself. It can identify patterns and learn from them to adapt behavior for future occurrences, while data mining is used as an information source for machine learning to pull data from. Furthermore, data mining can’t automatically see the relationship between existing pieces of data with the same depth that machine learning can.
Data mining can reveal patterns through classifications and sequence analysis. However, machine learning applications take this concept a step ahead by using the same algorithms to automatically learn from and adapt to the data collected. As malware becomes a pervasive problem, machine learning can look for patterns in how data in systems or the cloud is accessed. Machine learning also looks at trends to help identify which files are malware, with a high level of accuracy.