Classification vs Clustering

Why they differ

Classification sorts into groups that people named in advance and taught with examples; clustering finds its own groups with no names given.

Classification

Machine learning basics

Teaching a computer to sort each new case into one of a fixed set of groups, such as approve or reject, or real attack or false alarm.

Formal

A task in machine learning where the model learns from labelled examples to give each new input one label from a known, fixed list, often together with a score for how sure it is.

In plain English

Like a coin-sorting machine at a bank; every coin drops into the slot for its value, and there is no slot for a coin it has never seen.

In practice

At a shipping company, a model trained on thousands of invoices a bookkeeper had already sorted puts each new invoice into one of 40 cost types, such as fuel or harbour fees, and sends the unsure ones back to her.

Why it matters

No model sorts every case right, and wrong calls differ in cost (a missed attack usually costs more than a false alarm), so someone must decide which mistakes to accept and where a person checks.

Clustering

Machine learning basics

Letting a computer put similar items into groups by itself, with no names or right answers given in advance.

Formal

A task in machine learning that splits unlabelled items into groups so that items in the same group are more alike than items in different groups; the number and meaning of the groups are not given.

In plain English

Like looking up at the night sky and seeing which stars sit close together; the groups come first, and the names for them come afterwards.

In practice

A data analyst at a Danish online shop groups 80,000 customers by what and when they buy and finds six groups, including one that only ever shops during sales.

Why it matters

It finds order in piles of data nobody has time to label, but the groups carry no meaning until a person looks at them, and a different setting can split the same data quite differently.