The days of the Gold Rush are long gone and forgotten. We have a new kind of rush. Nowadays, companies are in search of another elusive prey - nuggets of information. The term data is trumpeted and embraced from small startups to huge corporations. The possession of the right type of information and the ability to use it can salvage and boost many a company. Therefore, this science deserves our attention.
We have already discussed the topic of the pros and cons of big data for businesses. In this blog, you’ll learn 8 types of data mining techniques in business analytics and their valuable input for artificial intelligence (AI) and machine learning (ML).
What is data mining? It’s the process of examining underlying and potentially useful patterns in big chunks of source data. Similar to precious-stone mining, in statistics analysts extract fragments of potentially useful information from the deep recesses of database systems. Here a goal is set to discover connections between the informational streams that weren’t perceived previously. Data mining has other names: knowledge discovery, information harvesting, etc.
You need to know that data mining employs machine learning (ML), artificial intelligence (AI), statistical information, and database technological insights. The gems of data mining can be applied for fraud detection and publicity purposes, etc.
The purpose of data mining is twofold:
So what data mining techniques do analysts use?
This data analysis is implemented to regain vital and actual information. It’s considered to be a complex data method among other latest data mining techniques. Information is classified into different classes. For instance, credit customers can be classified according to three risk categories: “low,” “medium,” or “high.”
Cluster analysis is a bit different classifying in the sense that here the pieces are grouped according to their similarities. For instance, different groups of customers are clustered together to find similarities and dissimilarities between the strands of information about them.
This data mining tool is designed to pinpoint and analyze the interactions between different variables. It’s used for identification of the probability of a particular variable from other variables’ existence. This method is also known as predictive power.
Regression analysis is also used to foresee the future value of a specific entity (the given feature could be either linear or nonlinear). Regression techniques of data mining are rather advantageous, due to the power of neural networks which is a unique method that emulates the neural signals in the brain. Ultimately the goal of regression is to show the links between two pieces of information in one set.
This mining data technique is used to find an association between two or more events or properties. It drills down to an underlying model in the database systems. Somewhat similar to buying a laptop - you are immediately offered to buy a bag to go with it.
This a process of identifying certain anomalies (outliers) in the data set. You need to be able to explain why there are these outliers amidst the all-encompassing pattern. For example, among your male audience of buyers, you have a sudden peak in female buying activity.
Prediction is considered to be an essential data mining technique. We all want to know the future value of our investments and to be protected from fraudulent crooks while online shopping. So it’s applied to forecast different types of data mining in the days to come. Analysis of the previous events can help to project more or less accurate predictions tomorrow.
You never know if a person will be honest two days from now but based on their previous credit history, you can surmise that if they’ve been people of integrity so far, then probably they will continue in their honest dealings with the bank for the months to come. Do you remember receiving a call from the bank clerk asking, do you want your credit limit increased? Well, that always sounds pleasant to be a trustworthy person.
This type of data analysis seeks to find out the same models, regularities or transaction tendencies in informational strands over a specified period. In sales, businesses can identify when some items are bought together during a particular season of the year. Based on this, companies offer better deals to those clients that have an actual purchasing history.
This type of data mining tool is used quite often as it’s the simplest for understanding. At the root of such decision trees, there is a simple question with many possible answers. Based on the responses, we can get the final answer to the central question. For example, we can attempt to respond to the following question: Should we play golf today?
Kicking off at the root box, if the weather forecast promises to be overcast, then might play golf today. If it’s going to be rainy, but it’s not raining yet, we could play provided it’s not too windy. If the weather is sunny, we should play golf if the humidity isn’t high. Such schematization helps to choose the best options among the good ones.
Let’s see how data mining techniques supplement AI and ML in your daily life.
Both AI and ML are alive and kicking in our world, and the credit for this belongs to data mining. Is it possible to create a true AI without feeding computers with a lot of relevant information and patterns? By no means! Actual data can be extracted only via data mining.
One of the most frequently encountered examples of data mining techniques is a recommendation system. Have you had an experience of buying two products on Amazon instead of one? How come?
The simple answer: Amazon did an excellent job on studying and analyzing your past purchasing history. Based on your preferences its AI prepares recommendation lists for you.
The reverse is also possible here in this process. You can first develop a theory and then strengthen it gradually through careful data analysis. For example, a self-driving car sees a red Ferrari exceeding the speed limit by a lot; it might create a hypothesis that all red Ferraris overspeed. This theory can be either strengthened or weakened later on.
Now let’s see how ML fits into the whole picture of the data mining process schematically:
As seen from the chart, ML is primarily applied in the modeling stage. It happens when analysts utilize a diversity of ML tools to make a pattern for existing problems or phenomena. The models typically depict interactions between many variables.
Additionally, association rule learning is used as an ML tool for digging up interesting connections between different information streams in large database systems. These rules are unearthed based on a certain level of interestedness.
Companies have to deal with data mining eventually. Its techniques aren’t just optional to know; they need to be mastered and consistently applied. AI and ML go hand in hand with digging up the right type of information. Data mining tools assist AI and ML drastically. This topic can help you select wisely regarding your future investments or maybe even change course with your current career.
There are many types of data mining software which help companies mine relevant information. Feel free to contact us if you need a consultation on how we could create data mining software that is tailored to suit your company’s needs! After all, data mining not only can enhance your business but also save it on rainy days.