Mattingly "Data Mining Techniques: Classification and Clustering"

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Text and Data Mining
Classification and Clustering

1. Classification
2. Clustering
3. Dimensionality Reduction Applied
Goals

Classification

Classification
Binary Classification
Multiclass Classification
Multilabel Classification
Hierarchical Classification

Binary
Classification
Definition
Binary classification is the simplest
kind of classification problem. In a
binary classif...

Binary
Classification
Example
“I hated that movie!”
Classes:
POSITIVE
NEGATIVE

Binary
Classification
Example
“I disliked that movie!”
Classes:
POSITIVE - 0.0
NEGATIVE - 1.0

Binary
Classification
Example
“I hated that movie!”
Classes:
POSITIVE - ?
NEGATIVE - ?

Multiclass
Classification
Definition
Multiclass classification involves
classifying instances into one of
three or more cl...

Multiclass
Classification
Example
Create the Classes

Multiclass
Classification
Example
DOG, CAT, HORSE

Multiclass
Classification
Example
DOG - 1.0
CAT - 0.0
HORSE - 0.0

Example
DOG - 1.0
CAT - 0.0
HORSE - 0.0

Multi-label
Classification
Definition
Multilabel classification differs from
binary and multiclass classification
in that ...

Multi-label
Classification
Example
The NASDAQ rebounded Today
Classes:
Technology
Finance
Sports

Multi-label
Classification
Example
The Chicago Bulls won 100-92
Classes:
Technology
Finance
Sports

Multi-label
Classification
Example
Quantum computers for the home?
Classes:
Technology
Finance
Sports

Multi-label
Classification
Example
Apple stock soars with new M3 chip
Classes:
Technology
Finance
Sports

Hierarchical
Classification
Definition
Hierarchical classification problems
involve categories that are related
to each ot...

Hierarchical
Classification
Example
● D - World History and History
of Europe, Asia, Africa,
Australia, New Zealand, etc.

Hierarchical
Classification
Example
● D - World History and History
of Europe, Asia, Africa,
Australia, New Zealand, etc.
...

Hierarchical
Classification
Example
● D - World History and History
of Europe, Asia, Africa,
Australia, New Zealand, etc.
...

Things to
Consider
Challenges
● What is the problem you are
trying to solve?
● Is there an existing model?
● How much data...

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