Classification in Data Mining

Focused businesswoman looking up at projection screen
Hero Images/Getty Images

Classification is a data mining technique that assigns categories to a collection of data in order to aid in more accurate predictions and analysis. Also called sometimes called a Decision Tree, classification is one of several methods intended to make the analysis of very large datasets effective.

Why Classification?

Very large databases are becoming the norm in today's world of "big data." Imagine a database with multiple terabytes of data —a terabyte is one trillion bytes of data.

Facebook alone crunches 600 terabytes of new data every single day (as of 2014, the last time it reported these specs). The primary challenge of big data is how to make sense of it.

And sheer volume is not the only problem: big data also tends to be diverse, unstructured and fast-changing. Consider audio and video data, social media posts, 3D data or geospatial data. This kind of data is not easily categorized or organized.

To meet this challenge, a range of automatic methods for extracting useful information has been developed, among them classification.

How Classification Works

At the danger of moving too far into tech-speak, let's discuss how classification works. The goal is to create a set of classification rules that will answer a question, make a decision, or predict behavior.To start, a set of training data is developed that contains a certain set of attributes as well as the likely outcome.

The job of the classification algorithm is to discover how that set of attributes reaches its conclusion.

Scenario: Perhaps a credit card company is trying to determine which prospects should receive a credit card offer.

This might be its set of training data:

Training Data
NameAgeGenderAnnual IncomeCredit Card Offer
John Doe25M$39,500No
Jane Doe56F$125,000Yes




The "predictor" columns Age, Gender, and Annual Income determine the value of the "predictor attribute" Credit Card Offer. In a training set, the predictor attribute is known. The classification algorithm then tries to determine how the value of the predictor attribute was reached: what relationships exist between the predictors and the decision? It will develop a set of prediction rules, usually an IF/THEN statement, for example:

IF (Age>18 OR Age <75) AND Annual Income >40,000 THEN Credit Card Offer=yes

Obviously, this is a simple example, and the algorithm would need a far larger data sampling than the two records shown here. Further, the prediction rules are likely to be far more complex, including sub-rules to capture attribute details.

Next, the algorithm is given a "prediction set" of data to analyze, but this set lacks the prediction attribute (or decision):

Predictor Data
NameAgeGenderAnnual IncomeCredit Card Offer
Jack Frost42M$88,000 
Mary Murray16F$0 




This predictor data helps estimate the accuracy of the prediction rules, and the rules are then tweaked until the developer considers the predictions effective and useful.

Day to Day Examples of Classification

Classification, and other data mining techniques, is behind much of our day-to-day experience as consumers.

Weather predictions might make use of classification to report whether the day will be rainy, sunny or cloudy. The medical profession might analyze health conditions to predict medical outcomes. A type of classification method, Naive Bayesian, uses conditional probability to categorize spam emails. From fraud detection to product offers, classification is behind the scenes every day analyzing data and producing predictions.

mla apa chicago
Your Citation
Chapple, Mike. "Classification in Data Mining." ThoughtCo, Dec. 29, 2017, Chapple, Mike. (2017, December 29). Classification in Data Mining. Retrieved from Chapple, Mike. "Classification in Data Mining." ThoughtCo. (accessed January 19, 2018).