What is the goal of descriptive data mining?

Describe general properties and patterns of existing data

Build a model from labelled training data only

Which learning type goes with each approach?

Descriptive: mostly unsupervised; predictive: mostly supervised

Descriptive: supervised; predictive: unsupervised

Year not given

Descriptive vs predictive data mining

Short answer

Data mining discovers interesting, previously unknown patterns from large data sets. Descriptive mining describes existing data using clustering, association and summaries, mostly unsupervised. Predictive mining builds models from training data to predict unknown values using classification, regression and forecasting, mostly supervised. The goal of descriptive is what happened; predictive is what will happen.

What is Data Mining? Differentiate between descriptive and predictive Data Mining.

Data mining is the process of discovering interesting, previously unknown and useful patterns from large data sets, using techniques of statistics, machine learning and databases.

Point	Descriptive data mining	Predictive data mining
Goal	Describe the general properties of existing data	Predict unknown or future values
Question	"What happened? What patterns exist?"	"What will happen?"
Approach	Presents the data as groups, rules and summaries	Builds a model from training data and applies it to new data
Learning	Mostly unsupervised	Mostly supervised
Techniques	Clustering, association rules, summarisation, sequence discovery	Classification, regression, time-series forecasting
Output	Clusters, rules, characteristics	Class label or numeric value
Example	Group customers by buying behaviour; find that bread and butter are bought together	Predict whether a loan applicant will default; forecast next month's sales

✓ Answer checked by Claude Opus 5.5 (high effort)

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