Data Mining vs Machine Learning

Data Mining vs Machine Learning: Major 10 Differences You Should Know

With the growth of the digital world, numerous unique terms and phrases have popularized that it is obvious to get affected or lose track. At the same time with the development of Big Data and analytics over the last few years meant that businesses are grappling with an entire unique lexicon of tech terminology. Undoubtedly it may become the reason for confusion, as the majority of the people are not sure about the difference between such unique terms and approaches. We can say Data Mining vs Machine Learning is the best example of this.

In this blog our we are going to discuss both data mining and machine learning in detail. So if you have any confusion regarding Data Mining vs Machine Learning, this blog is for you.

What Is Data Mining?

A data mining process is sometimes called KDD (Knowledge Discovery Process). It is noteworthy here that the term KDD(Knowledge Discovery Process) was firstly coined by Gregory Piatetsky-Shapiro for the first workshop on the same topic KDD(Knowledge Discovery Process), in 1989. After that, the term KDD became most popular in Machine learning and AI. The term data mining was first used in the database community in 1990.

Data mining is generally considered to be a science that can be used to determine different properties of a dataset. In data mining, all the large datasets are usually collected from the data warehouses or RDMS or complex datasets such as spatial, series, etc. The primary purpose of doing this collection is to take out interesting patterns and correlations among the different data items. After that, the results that come from the data mining process are used to improve the business processes.

See also  Get the comparison of Perl vs Java in tabular form in detail

What Is Machine Learning?

Arthur Samuel was an American pioneer in the field of artificial intelligence and computer gaming. Machine learning was first coined by him in 1959.  He also said that “machine learning gives computer systems the capability to learn without explicitly programming.” Machine learning is a modern technique that develops compound algorithms for processing extensive data and provides accurate results to the users.

Machine learning generally uses different types of complex programs that can quickly learn through experience and create predictions. In machine learning, the algorithms are mainly improved by themselves through a routine of the training data. The primary purpose of machine learning is to quickly understand data and build required models from the available data that can be easily understandable and easily useable for users.

Data Mining vs Machine Learning: What Are The Similarities 

There are numerous similarities between Data Mining and Machine Learning that why most peoples seem confused about Data Mining and Machine Learning. Here we have mentioned some similarities between Data Mining vs Machine Learning:-

  • Data Mining and Machine Learning both are excellent at pattern recognition
  • Data Mining and Machine Learning both need a large number of datasets to provide accurate results
  • Data Mining and Machine Learning both are considered one of the best analytics processes
  • Data Mining and Machine Learning are related to learning from datasets to improve decision-making for a business.
A+ grade

Data Mining Vs Machine Learning: What Are The Differences

As we have already discussed the similarities between Data Mining and Machine Learning. But still, it is common to confuse both two terms because of the overlap of datasets. On the other side, there are numerous differences between Data Mining and Machine Learning. Here we have mentioned the key differences between Data Mining vs Machine Learning:-

See also  Python vs Perl | What are the differences?
FactorsData MiningMachine Learning
WorkingData mining is the method of mining deep into datasets to find out helpful information.Machine learning is the technique of growing complicated algorithms for making machines sufficiently perfect by supporting them with well-trained datasets.
ConceptThe central concept behind data mining is to extract data using different methods and take out the patterns and trends.On the other hand, the central concept behind machine learning is that machines learn from the available data and improve by themselves. 
NatureUsually, data mining requires manual intervention for applying methods to extract information from available data. Machine learning is entirely different from data mining as it learns automatically, without any human intervention.
ImplementationData mining includes the construction of models on which different data mining techniques or methods are applied. Models such as the CRISP-DM are built through data mining. The entire data mining process includes using a data mining engine, database, and pattern evaluation for knowledge discovery.On the other hand, machine learning is implemented using different algorithms in neural networks, decision trees, artificial intelligence, neuro-fuzzy systems, etc.
ApplicationsMachine learning might produce results on a larger amount of data, but data mining may produce results on a lesser volume.

Compared to data mining, machine learning generally requires a large amount of data to provide accurate results.
ScopeThe main purpose of data mining is to discover how numerous datasets’ properties are linked through data visualization techniques and patterns. In simple words, the primary aim of data mining is to find out the actual relationship between more than one attribute of a specific dataset.The primary purpose of machine learning is to predict numerous outcomes like time duration approximation or price estimate. Machine learning automatically reads the model with experience over time. It also provides actual-time feedback to users.
UsesGenerally, data mining is used in research fields like text mining, web mining, and fraud detection. Commonly, machine learning is used to estimate the time required for delivery, create recommendations of prices or products etc.
MethodData mining usually performs analysis in a group format at a specific time to generate results. On the other hand, machine learning usually uses data mining methods to change its behavior to future inputs and improve algorithms. Thus, we can say that data mining acts as a data source for machine learning. 
Learning CapabilityData mining is considered a manual technique as it requires the analysis to be initiated by human resources.Machine learning is considered a step advanced from data mining as machine learning uses similar techniques used by data mining to learn and adjust to changes automatically. Along with this, machine learning is considered more reliable than data mining.
AccuracyThe accuracy of the results provided by data mining depends on how data is collected because data mining requires human intervention, so there are chances of missing important relationships.Machine learning algorithms are considered more reliable or accurate than data mining methods or techniques.

Final Words

We have already discussed Data Mining vs Machine Learning in detail. Thus we hope that our blog will become very helpful for you and it will clear all your doubts regarding Data Mining and Machine Learning. Suppose you have any confusion regarding Data Mining vs Machine Learning.

See also  What are The Uses Of R Programming Language

Even if you are a student and are troubled with your Data science assignment, then don’t worry. We are here providing the best Data science assignment help at very reasonable prices to students around the world. 

Frequently Asked Questions

Is data mining important for machine learning?

Generally, data mining is considered one of the essential parts of machine learning. Since it is used to find out valuable hidden trends and patterns within vast volumes of data, both machine learning and data mining employ modern algorithms to uncover related data patterns.

What is data mining used for?

Data mining is known as the process of finding patterns, correlations, and anomalies within large-scale datasets to predict the result or outcomes. With the use of a broad range of advanced techniques, one can easily use this outcome or result to cut costs, reduce risks, increase revenues, improve customer relationships, etc.

Leave a Comment

Your email address will not be published. Required fields are marked *