Please enable JavaScript.
Coggle requires JavaScript to display documents.
DW & DM - Coggle Diagram
DW & DM
📌1. Data Mining Definitions
analyzing large amounts of data to discover useful information.
analyzing large amounts of data to discover useful information.
used in marketing / healthcare / education / product development
Helps companies:
Solve problems/ reduce risks / find new opportunities / improve decisions
📊2. Typology According to the Objective
1. Classification
Assigns an object to a known class.
Uses supervised learning.
Examples:
loan approval
sick / non-sick patient
customer ranking
2. Prediction
Predicts future values using existing data.
Uses known attributes to estimate unknown results.
Examples:
predict customer quality
predict interested customers
3. Association
Finds relations/correlations between attributes.
Uses unsupervised learning.
Example :
market basket analysis
Goal:
cross-selling
attractive product grouping
4. Clustering
Groups similar data into clusters.
Uses unsupervised learning.
Classes are unknown.
Examples:
market segmentation
tumor localization
📌 3. Typology According to Learning Type
Supervised Learning
Training data contains :
input + output (known class)
Goal:
classify new examples correctly
Used in:
classification / prediction
Unsupervised Learning
Training data contains only input data.
Goal:
discover groups or relations automatically
Used in:
clustering / association