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The Difference between Mining and Machine Learning

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Data mining has been in existence for years but comes in the public eye in 1930’s.The data extraction technique has been in existence for over a century now. According to Hacker Bits, one of the first occasions of data mining happened in 1936 when Alan Turing invented the universal machine that could perform similar duties as computers in today’s time.

Forbes also reported that the development of Turing known as the ‘Turing test’ in 1950, was to determine whether computers had some intelligence. In the test, a computer had to fool humans so that they could conclude that indeed it had some intelligence.

For sure data mining and machine learning have come from a long way. Today, Companies have adopted the use of data mining and machine learning in activities like expanding the markets and making profits. Data mining and machine learning depend heavily on data. Generally, they are mistaken to be the same. Let’s see some of their differences and the manner to use them.

Data usage

The main difference between data mining and machine learning is how they are used and applied. Data mining is regularly used by machine learning to connect different relationships. For example, Uber use machine learning to measure ETAs to calculate the cost of rides or food delivery times.

Data mining is used in many activities like financial research where investors might use it to determine the best institutions to provide funding. A business can use data mining to collect data on market trends which can be used in securing new leads. Also, Data mining can also be used to look carefully through social networks to gather information to be used in the promotion campaign.

However, Machine learning incorporates data mining but can also make involuntary relations and learn how to use new algorithms. This is the same technology that is used by automatic cars that adjust themselves to new conditions. Machine learning is also the reason behind instant recommendations on Amazon.

Machine learning is also the technology used in banks to detect fake money. This has greatly helped to control fake money and frauds in banks today.

Foundation of learning

Data mining and machine learning both share the same background, but their approach differs. Data mining engages in existing data to help in making decisions, for example, a Cloth line free people uses data mining to come up with the latest fashion.

On the contrary, machine learning can get the ideas from existing data and then uses it to teach itself. Zebra Medica vision advanced a machine learning algorithm to foresee cardiovascular conditions which cause death to more than 500,000 people in America annually.

Machine learning can also analyze events and learn behavioral adaptation for future while data mining is essentially used as data source for machine learning. Data mining requires human synergy to learn and use the knowledge. Data mining can be configured automatically to search for data, but it can’t apply the information without human help. It also can’t get the connection between existing data while machine learning can.

Pattern Recognition

Analyzing and interpreting data requires the right software and tools in order get the right patterns. Or else, people will waste time trying to search for these complicated patterns. Companies can use this information in sales projection, for example, Wal-Mart collects data from their outlets. They later use the information to determine to buy patterns and direct their prospective predictions.

Through analysis and grouping, data mining can identify some patterns. Nonetheless, machine learning uses the same information to learn and adapt to the collected information.

As malware has become a threat, machine learning can help to identify malicious software by accessing the system.

Enhanced Precision

Data mining and machine learning both help to enhance data accuracy. However, data mining involves collecting and organizing information. Data mining can be used to get accurate information, and this clarifies machine learning for fine results. A human may fail to notice data correlations, but machine learning will connect this for best results and improve on the machine norms.

Machine learning can be used in marketing to help salespeople to comprehend their clients. CRM systems together with machine learning can examine the past events leading to customer satisfaction feedback. Also, it can be used to depict which products and services sell better and marketing feedback to the clients.

The future of data mining and machine learning

Forbes reported that by 2020, the global data would increase from 4.4 to 44 zettabytes, and 1.7 megabytes of data will be created each time for each human on earth.

As you gather information, there will be high demand for improved data mining and machine learning techniques to help keep up with demand and reliability. With time data mining and machine learning will intersect to improve harvesting large amounts of data.

According to BIO-IT World, the future of data mining will result in predictive analysis and also advances analysis in different industries. For example, in the medical industry, scientists will be able to use predictive analysis to examine factors associated with a disease and the treatment that will work best.

Also, according to Geekwire, thousands and thousands of machines will be connected, and this can be improved by use of IoT technology.

Some professional argue differently about data mining and machine learning. They argue that there is no need to look at the differences between data mining and machine learning, but get to understand how we can learn from data. The important thing is ‘collecting data and how to benefit from it.’