In simple words, we can say data mining is the process of predicting the future from the knowledge of past and present. It is a branch of Data Science. It is also known as Knowledge Discovery in Data (KDD) – Oracle. We have billions of TeraByte of data coming from all around the world. Now it’s up to us to fetch the knowledge from the data.
It’s an old saying “We are drowning in data but starving for information” – Oracle.
Data mining is a combination of many disciplines. It includes Statistics, Pattern Recognition, Artificial Intelligence, Machine Learning, Computer Vision, etc. It is used by many companies all around the world to take data and convert it into useful information for Business Intelligence. Companies like Facebook, Google, Microsoft, Amazon, etc. take user data and use it to find very useful information about their users.
Data Mining has 100s of examples all around the world. It is such a big leap in technology that its examples and use cases are countless. But here are some important examples.
It is the most common example of Data mining and Data analytics. Google trends take user searches data and shows which keyword is searched by users all around the world.
It’s a very old method that uses data mining. This method is used to provide necessary information about user purchasing habits. This method shows what the user buys the product simultaneously. In recent history, the corporation named Pepsico ltd. finds out that users love to buy Pepsi cola with Lays chips. They start marketing and branding Lays and Pepsi goes hand in hand. This process is used to penetrate the mind of users.
The financial sector is the place that has most benefited from Data Mining. With the help of pattern recognition and data analytics, they can identify when the price of stocks and shares is going up and down. By having this much power in their hands they can earn millions of dollars in profit. They simply can purchase assets when their price is low and can sell them at a high price later on.
Bioinformatics deal with gathering, storing, and performing operations on collected data to get some useful information about medicines and might be able to tackle future diseases. Data Mining can increase the productivity of bioinformatics. It collects data from millions of different sources, integrates it, and converts it into useful information.
It is also known as the KDD process. It is the process to convert data into knowledge for Business Intelligence.
Data Mining process includes taking input data, data pre-processing, data mining, data post-processing, and then Knowledge.
As you know that data mining is a very vast field having multiple disciplines. It will be difficult to describe all types in just one article. But some main types will be described here.
This rule is as simple as an if-then-else statement. It also includes probability and statistics in it.
Yes, we know it seems difficult at first but let me explain with formulae and an example.
Milk -> Butter
X= Milk
Y = Butter
N = Total Number of Transaction
It is a classic technique used in machine learning. It is a supervised learning method. It is the process of creating a model that distinguishes data and its classes. It is used to classify the data and divide data into different groups. It is simple like that to train models that classify between male and female depending upon their gender.
There are many classifiers available for data mining classification. Main two classifiers are Lazy Learners and Eager Learners
It is unsupervised learning. It is a process of dividing data into groups. Every element is a group that must be the same type and every element of a different group must be different.
The clusters don’t need to form a circle or oval shape, they can form any shape.
It is the process of finding frequent patterns within the given data. It is also known as association rule mining.
It is the process of identifying and removing any noise or anomalies in the data. The data that we collect from all over the world has multiple issues and errors. The data is dirty. It is a long and lengthy process. We simply cannot enter the data for processing. The dirty data will generate wrong patterns. It will definitely hurt the business process.
At this stage, the data is being aggregated. It is the process of data mining to achieve more and accurate information. This technique is mostly used to give a birds-eye view of the business process. This technique is very popular in big data analysis.
This method uses the bottom-up approach to see the data generally. It’s like we sold 1000 mobiles in Chicago, 900 mobiles in New York, 1500 mobiles in Los Angeles, 2000 mobiles in Houston from America. We sold 1000 mobiles in Toronto, 800 mobiles in Montreal, and 2400 mobiles in Ottawa. It’s data. Its generalization will be that we sold 5400 mobiles in the USA and 4200 mobiles in Canada.
There are many features regarding the data. For an employee, we can have the name, age, gender, qualification, experience, phone number, house number, email, family members, etc. As an employer, the thing I am more concerned about is his/her qualification and experience. This is the process of feature selection where I take the features I like and reject the others.
It is the process of finding the object that is not suitable for the environment. Like in office the salary range of all employees is from $400 to $2000. We found a record that shows there is an employee whose salary is $10000. So, this seems to be an outlier and needs to be accurate or removed.
This technique is used to identify the chance of something happening. Like we can identify the chance of a surge in the prices of different goods due to the increase in petrol prices.
It’s like the brain of a human. Human brains have neurons firing all over the brain whenever he/she starts thinking. The human mind has the whole network of neurons connected together that creates human thinking ability and intelligence. Just like that in this branch of data mining, the computer processors are connected together with the network all are sending and receiving data from each other and creating intelligence.
Data mining is important to extract knowledge from the data. A simple database has the ability to create subsets of data from original data.
Data S={1, 2, 3, 4, 5, 6, 7, 8, 9, 0}
Database={1, 3, 5, 7, 9} and {0, 2, 4, 6, 8}
Data: Will he be able to pass the exam this time? He is trying again and again.
Database: Error
Data Mining: Yes, by identifying his past activities. His progress is continuously going up. This time there are 90% chances he will pass the exam.
Businesses are adapting slowly but steadily. Data mining can be beneficial for the future. The business that has the future in mind and has a goal to achieve can only succeed in the future. See Facebook, Apple, Microsoft, etc. all their owners have a vision and can see in the future. Data mining will be the future of industries and businesses.
We have multiple sources from which we can get information now. We have social media, we have hypermedia and other mediums to get information. We have videos, audios, hypertext, images, and many things available on the internet. Not with the help of data mining we can get all the information and create a new platform. We just need to have a vision.
It is the process of getting data from mobile devices. Not just our smartphones or tablets but also our fitness bands, automobiles, etc. We can get information regarding human and computer interaction and can identify how humans treat machines. Make the interaction better.
Distributed computing is a very popular technology. In this technology, we can perform single tasks on multiple computers simultaneously and increase the performance of our devices. In decentralized data mining, we take distributed computing and take it to another level to extract information from different computers which are working on the same problem a couple of times. To make it more agile and boost performance.
This is a new and very intriguing trend in mining. In this process, we take geological, environmental, and astronomical data like images, sound, atmosphere and use it to become more knowledgeable about how things work and how we can stay safe in this environment.
This is a new trend and Google is using it for a couple of years. By seasonal and time series data mining we can get data about seasons. We can identify when and where there is going to rain. Where is the possibility for tornadoes coming and many more that help to save a life?
You can see more in detail from FlatWorldSolutions.
You can get more information regarding its pros and cons from Educba, Zentut, and Wisestep.
You can see more detailed differences in data mining and machine learning from SimpliLearn and from Educb.
Yes, Data Mining will definitely impact the future a lot. For both better and worse. Ithas revolutionized the present and will revolutionize the future. Its industry and business are booming due to Data Mining and the knowledge is extracted. Everything has its advantages and disadvantages. Where data mining is making the future of the business industry and IT industry bright, it will also be used for bad purposes in the future. Like the internet was made to connect different organizations, like social media was made to bring the people together. Now both internet and social media is being used for corruption and law violation. We can see a very bright future in data mining. But be careful how and when we can use it. It can create and destroy businesses due to its intelligent behaviors. You can get more information about data mining past, present, and future from researchgate.
