Easiest Way to Know How Data Mining Works

EM Admin
Dec 15, 2019
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Data is a new buzzword. What’s in it that every industry talks about it?

We must say that an abundance of knowledge underlies it. You could easily get through what’s next.

To know how, let’s get through the explanation about data mining in a super easy way.

Data Mining with Examples

As the phrase suggests, the data mining is the process of filtering the piles of information to get an insight. Or, it’s the process of extracting knowledge from the data. This incredible technique lets you to whip out an algorithm without carefully selecting the data, churning in the right way and considering how to interpret the result. It’s just an applied statistics that involves searching lots of data points for patterns, which a human eye might not spot. Those patterns are based not on human intuition, but on what the data suggest. It’s similar to meteorology, wherein weather forecasting is built up on the concrete base of climatic statistics.

Consider another example. The app viz. CarePassport mines healthcare data through consolidated medical records of the medicine providers, imaging CDs, Apple watch, health kit, fit bit and any CDA file or documents. The gathered medical records let the app analyze for aggregating and accessing their medical images, lab results, dental records, clinical reports and a lot more by the patients.

Role of Data Mining

This digging of data can accelerate to every kind of knowledge that you look for, such as curating Facebook feeds or optimizing an e-store layout. As a data analyst or scientist, you can easily achieve description and prediction by analyzing quantum of data, but not through careful study. In short, the mining of data is more about spotting patterns than explaining them.

In case of Netflix, let’s say, the data mining might mean scanning for patterns in the genre labels or titles, internet reviews and anything else about each track. This is all done while keeping age, location, the friend group and many other bits of information into account.

Data Mining Techniques & Their Working Process

Classification: Being most broadly applicable data mining technique, classification is all about categorizing the data for detecting fraud or churn or product offers or segmenting user. The case of “Target” highlighted the significance of this technique, assigning each customer to “Probably Pregnant” and “Probably Not Pregnant” categories.

The data are wisely broken down into a collection of numerical, attributes or features. For the example “Target”, an instance might be an expecting mother. The things like how many times she ordered for zinc supplements or any other product in her inventory define features & labels for this process. Upon upping the data online in the data warehouse, training comes up. This is how the system teases out patterns from all the labeled examples.

How it works:

In essence, the classification works on algorithms. It works best on the basis of different factors, like how many groups are there, how different weighed features are linked to each other.

Regression: However, it’s a close cousin of classification, but there is a slight difference. Rather than predicting a category, it helps to predict a number. In the case of an insurance company, the classification emphasizes on the instance, like an adult or an aged man. On the flip side, regression determines when that instance is going to die and when they should start counseling for it. It helps to manage due dates also.

How it works:

A dozen or may be thousands of variables (the features that describe each instance) support regression process to figure out, say, how far away you would expect the customer’s due date to be.

The most known example is the “Google Flu Trend”. In 2008, the real time publishing highlighted how many people had this illness, which was driven from the number of searches for the words, like “fever” and “cough”.

Clustering: It is about grouping the data points in a way that they help you with the analysis. It’s as what Amazon or eBay has done on its website. Millions of products are organized logically in categories and sub-categories, despite having an overwhelming selection. Manually, it’s a painful process. But with clustering, it’s no big deal.

It automatically groups the products.

How it works:

Anomaly Detection: It is about identifying instances that are dissimilar, unusual or worrisome. Anomalies are basically recognized as inconsistencies or oddities. Many credit card companies use it to flag transactions that do not mix and match the features of usual buying habits.

How it works:

Anomaly Detection: It is about identifying instances that are dissimilar, unusual or worrisome. Anomalies are basically recognized as inconsistencies or oddities. Many credit card companies use it to flag transactions that do not mix and match the features of usual buying habits.

How it works:

In the nutshell, the data mining process spotlights the unseen patterns that have profound value. This value can be a business intelligence that proves a breakthrough in the entrepreneurship.