Data Mining Vs Machine Learning

EM Admin
Feb 12, 2021
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Data and analytics are taking a new turn every day. They have completely changed the way to carry out business intelligence, research and strategy making. This makes an average business user to struggle with many challenges because of ever-changing technology.

Mostly, users get confused for being unknown to the difference between data mining and machine learning.

Let’s come across what these terms are and how they differ from each other.

Data Mining

Data mining refers to figuring out useful and most meaningful models that later qualify to become algorithms. The modeling brings unknown patterns, relationships and anomalies to limelight from complex data warehouses.

This modeling discovers the change to be put on automation, which simplifies complex and lengthy processing for business intelligence. The picked up insights that have a ton of breakthroughs, which can change the way one typically perform.

Machine Learning

This is a part of artificial intelligence (AI), which makes systems think, analyse and respond corresponding to queries. It’s similar to the neural network of a human being. Simply put, it configures the thinking process for systems and machines, which data mining services company often do to achieve a brand vision and values.

The thought process continues to take place when the compute, let’s say, receives a query. The system itself analyzes large data sets and then, learns models or data patterns that help to predict about new data sets. It becomes learning for machine, which turns out as a response against the related query. The best thing is that it does not involve human interference, except for initializing the programming. The identified patterns interact with relevant queries to respond as per predicted patterns.

In short, machine learning is a learning system for computers, which teaches it to think as we do. As we interpret information and learn from whatever we do or observe, the machines rely on data to do so. Their analysis sets predictions on the basis of their value and usability.

For instance, whatever you see as a recommendation on Amazon or other ecommerce website is an epic spadework of machine learning. How does Netflix predict what delights you the most?

It is machine learning (ML) that helps its analytical process to come out with predictions about series that are very much close to realities. Banks tap to frauds using it and eliminate losses.

Matter of Confusion Between Data Mining and ML

The confusions take a rise from similarities. These two revolutionary data techniques have these points in common:

Both involves analytical processing
Data mining is all about recognizing patterns and ML highlights predictive patterns.
Both techniques are based on logical reasoning from data sources.
Both involve cleansing and processing of datasets to filter the most suitable patterns.
Both are inter-woven, encompassing processing for predicting patterns through automated analysis.

Key Differences

Despite being so common, data mining and ML have many things that set them a pole apart. Here we have these all:

Key Points	Data Mining	Machine Learning (ML)
Data As a Source	This process sticks around picking up patterns that already exist in the database.	It goes way beyond the set patterns, as data scientists look for new patterns that have a potential to become a prediction. However, this whole process needs pre-existing data.
Process	This process runs on some rules to push unknown patterns to recognition.	It follows a defined process that involves difference preset methods to understand and learn.
Automation	This process may use automated tools for deep research and discovering patterns. But, it all begins from human interference.	The start to finish processing takes place with automated systems that extract, transform and load datasets using automated intelligence.
Use	Certainly, this method is used on existing databases to determine useful patterns.	This process teaches computer to derive sense on data and then, come with predictions, which are later set as an algorithm for devices.