SLIDE 1
What to Expect
- Basic model
- Brief associated content
- No mathematical equations!
Teach Yourself Ant Clustering in Ten Minutes Karan K. Budhraja - - PowerPoint PPT Presentation
Teach Yourself Ant Clustering in Ten Minutes Karan K. Budhraja - - PowerPoint PPT Presentation
Teach Yourself Ant Clustering in Ten Minutes Karan K. Budhraja What to Expect Basic model Brief associated content No mathematical equations! Motivation Interest in nature inspired algorithms applied to Machine Learning
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SLIDE 3
Motivation
- Interest in nature inspired algorithms
○ applied to Machine Learning or Artificial Intelligence ○ explored in personal work
- Clustering
○ unsupervised learning ○ important in context of Information Retrieval
SLIDE 4
Definitions
- Clustering
○ grouping of objects based on similarity
- Swarm Intelligence
○ use many unintelligent agents to perform intelligent task ○ software agents ○ often nature inspired
SLIDE 5
Ants MVP! (for this presentation)
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Related Work
- Ant Colony Optimization
○ ant foraging behavior ○ ants use chemical trails ○ shortest path emerges
SLIDE 7
Ant Clustering in Nature
- Cemetery formation
○ form piles of corpses ○ keep nests clean!
- Brood sorting
○ sort minion ants
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Ant Clustering (Basic Model)
- 1. Scatter items randomly in 2D space
- 2. Ants begin to move
- 3. Ants pick up items with probability Ppick
- 4. Ants drop items with probability Pdrop
- Ppick increases in dissimilar item locality
- Pdrop increases in similar item locality
SLIDE 9
Ant Clustering (Standard Model)
- Basic Model + modifications
○ no communication between ants ■ distributed approach ○ short term memory ■ remember recent drop locations ■ use them while carrying item ■ move towards similar drops
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Variations (brief)
- Sense environment complexity
○ complexity based on item similarity in locality ○ agents focus on high complexity areas
- Sense environment / cluster density
- Sense ant odor
- Hybrid with traditional methods
○ K-Means clustering ○ Fuzzy C-Means clustering
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Applications
- General benefits
○ linear complexity with #features ○ #clusters / seed not specified ○ inherently parallel
- Anomaly detection
- Exploratory data analysis
SLIDE 12
Example
- Ant based clustering by
Christian Nitschke
- Example image
○ just in case video doesn’t
work :)
SLIDE 13
Future Work
- Comparison with traditional methods
- Exploration of real world applications
- Parameter tuning, sensitivity
- Combination with traditional methods
○ best of both worlds
SLIDE 14
Further Reading
- Plethora of references in document
OR
- Ignore everything and read this
○ Jafar, OA Mohamed, and R. Sivakumar. "Ant-based clustering algorithms: A brief survey." International journal of computer theory and engineering 2.5 (2010): 1793-8201.
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