Teach Yourself Ant Clustering in Ten Minutes Karan K. Budhraja - PowerPoint PPT Presentation

Dec 22, 2022 •309 likes •462 views

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

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 or Artificial Intelligence ○ explored in personal work ● Clustering ○ unsupervised learning ○ important in context of Information Retrieval
Definitions ● Clustering ○ grouping of objects based on similarity ● Swarm Intelligence ○ use many unintelligent agents to perform intelligent task ○ software agents ○ often nature inspired
Ants MVP! (for this presentation)
Related Work ● Ant Colony Optimization ○ ant foraging behavior ○ ants use chemical trails ○ shortest path emerges
Ant Clustering in Nature ● Cemetery formation ○ form piles of corpses ○ keep nests clean! ● Brood sorting ○ sort minion ants
Ant Clustering (Basic Model) 1. Scatter items randomly in 2D space 2. Ants begin to move 3. Ants pick up items with probability P pick 4. Ants drop items with probability P drop ● P pick increases in dissimilar item locality ● P drop increases in similar item locality
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
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
Applications ● General benefits ○ linear complexity with #features ○ #clusters / seed not specified ○ inherently parallel ● Anomaly detection ● Exploratory data analysis
Example ● Ant based clustering by Christian Nitschke ● Example image ○ just in case video doesn’t work :)
Future Work ● Comparison with traditional methods ● Exploration of real world applications ● Parameter tuning, sensitivity ● Combination with traditional methods ○ best of both worlds
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.

Download Presentation

Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

In 30 minutes, well teach you how to bid on a state contract o Why join the Small Business

State of Illinois Chief Procurement Office S MALL B USINESS S ET-ASIDE P ROGRAM Putting together the States Procurement Puzzle September 17, 2014 1 How Do I Win a State Contract? In 30 minutes, well teach you how to bid on a state

759 views • 13 slides

Lecture 23: Spectral clustering Hierarchical clustering What is a good clustering?

Lecture 23: Spectral clustering Hierarchical clustering What is a good clustering? Aykut Erdem May 2016 Hacettepe University Last time K-Means An iterative clustering algorithm - Initialize: Pick K random points as cluster

932 views • 64 slides

Clustering Hierarchical clustering, k-mean clustering Genome 559: Introduction to Statistical and

Clustering Hierarchical clustering, k-mean clustering Genome 559: Introduction to Statistical and Computational Genomics Elhanan Borenstein A quick review The clustering problem: partition genes into distinct sets with high homogeneity

772 views • 40 slides

Graph Clustering Graph Clustering What is clustering? What is clustering? Finding patterns

Graph Clustering Graph Clustering What is clustering? What is clustering? Finding patterns in data, or grouping similar groups of data-points together into clusters . Clustering algorithms for numeric data: Lloyds K-means, EM

688 views • 19 slides

Clustering Hierarchical clustering and k-mean clustering Genome 373 Genomic Informatics

Clustering Hierarchical clustering and k-mean clustering Genome 373 Genomic Informatics Elhanan Borenstein A quick review The clustering problem: partition genes into distinct sets with high homogeneity and high separation Many

768 views • 43 slides

Clustering DSE 210 Clustering in R d Two common uses of clustering: Vector quantization Find

Clustering DSE 210 Clustering in R d Two common uses of clustering: Vector quantization Find a finite set of representatives that provides good coverage of a complex, possibly infinite, high-dimensional space. Finding meaningful

396 views • 36 slides

Clustering: Hierarchical Clustering and K- Means Clustering Machine

Clustering: Hierarchical Clustering and K- Means Clustering Machine Learning 10-601B Seyoung Kim Many of these slides are derived from William Cohen,

660 views • 46 slides

Clustering and Dimensionality Reduction Preview Clustering K -means clustering

Clustering and Dimensionality Reduction Preview Clustering K -means clustering Mixture models Hierarchical clustering Dimensionality reduction Principal component analysis Multidimensional scaling Isomap

1.61k views • 17 slides

Clustering A Categorization of Major Clustering Methods Partitioning Methods

Clustering What is Clustering? Types of Data in Cluster Analysis Clustering A Categorization of Major Clustering Methods Partitioning Methods Hierarchical Methods 1 2 What is Clustering? What is Clustering? Typical

647 views • 18 slides

GOOD MORNING! 1. Find a partner. 2. Tell them about why you teach what you teach, and how you

GOOD MORNING! 1. Find a partner. 2. Tell them about why you teach what you teach, and how you teach it. 3. Have them tell you about why they teach what they teach, and how they teach it. 4. Take some notes about the experience. (Well come

1.11k views • 40 slides

Subspace Clustering Ensemble Clustering Subspace Clustering, Ensemble Clustering, Alternative

LUDWIG- MAXIMILIANS- INSTITUTE DATABASE UNIVERSITT FOR SYSTEMS MNCHEN MNCHEN INFORMATICS INFORMATICS GROUP GROUP Subspace Clustering Ensemble Clustering Subspace Clustering, Ensemble Clustering, Alternative Clustering, Multiview

513 views • 9 slides

Who are we teaching and Who are we teaching and how do we teach them? how do we teach them?

Who are we teaching and Who are we teaching and how do we teach them? how do we teach them? William O. Bond and John C. Mayer University of Alabama at Birmingham Greater Birmingham Mathematics Partnership 1 Sept 2008 Motivating Questions

518 views • 27 slides

Give the group a couple of minutes per question. Discuss feedback in Presentation skills plenary.

Small puzzle pieces make the big picture What we really teach when we teach presentation skills Lewis Lansford Lewis Lansford September 2015 The power of presentations How preparing & giving talks develops transferrable skills Lewis

117 views • 7 slides

Finding Clusters Types of Clustering Approaches: Linkage Based, e.g. Hierarchical Clustering

Finding Clusters Types of Clustering Approaches: Linkage Based, e.g. Hierarchical Clustering Clustering by Partitioning, e.g. k-Means Density Based Clustering, e.g. DBScan Grid Based Clustering Compendium slides for Guide to Intelligent

1.32k views • 82 slides

Introduction to Machine Learning, Clustering and EM Barnab s P czos Contents Clustering

Introduction to Machine Learning, Clustering and EM Barnab s P czos Contents Clustering K-means Mixture of Gaussians Expectation Maximization Variational Methods 2 Clustering 3 K- means clustering What is clustering?

1.32k views • 66 slides

PAC-Bayesian Analysis of Co-clustering, Graph Clustering and Pairwise Clustering Yevgeny Seldin

PAC-Bayesian Analysis of Co-clustering, Graph Clustering and Pairwise Clustering Yevgeny Seldin Motivation Example Clustering cannot be analyzed without specifying what it will be used for! be used for! Example Cluster then pack

293 views • 28 slides

Conserving Fitness Evaluations by Marking Used Transitions Daniil Chivilikhin and Vladimir

Learning Finite-State Machines: Conserving Fitness Evaluations by Marking Used Transitions Daniil Chivilikhin and Vladimir Ulyantsev National Research University of IT, Mechanics and Optics St. Petersburg, Russia Machine Learning Applications

743 views • 57 slides

MuACO sm A New Mutation-Based Ant Colony Optimization Algorithm for Learning Finite-State

MuACO sm A New Mutation-Based Ant Colony Optimization Algorithm for Learning Finite-State Machines Daniil Chivilikhin and Vladimir Ulyantsev National Research University of IT, Mechanics and Optics St. Petersburg, Russia Evolutionary and

817 views • 43 slides

Ant Colony Optimization By: Aaron Obernuefemann October 22 nd , 2012 Data Mining Methods MATH

Ant Colony Optimization By: Aaron Obernuefemann October 22 nd , 2012 Data Mining Methods MATH 3220 Overview Definition of Ant Colony Optimization (ACO) Terminology Metaheuristics The Algorithm ACO Example Summary

133 views • 12 slides

Data Mining using Ant Colony Optimization Thanks to: Johannes Singler, Bryan Atkinson

Data Mining using Ant Colony Optimization Thanks to: Johannes Singler, Bryan Atkinson Presentation Outline Introduction to Data Mining Rule Induction for Classification AntMiner Overview: Input/Output Rule Construction

512 views • 19 slides

Test-Based Extended Finite-State Machines Induction with Evolutionary Algorithms and Ant Colony

NATIONAL RESEARCH UNIVERSITY Test-Based Extended Finite-State Machines Induction with Evolutionary Algorithms and Ant Colony Optimization Daniil Chivilikhin, Vladimir Ulyantsev, Fedor Tsarev St. Petersburg National Research University of

635 views • 32 slides

Meta-heuristic Based Cloud Resource Provisioning Approach Agenda for Today Introduction

Meta-heuristic Based Cloud Resource Provisioning Approach Agenda for Today Introduction What is meta-heuristics? How is it different from heuristics? What makes them appropriate for resource provisioning as compared to heuristics?

1.2k views • 46 slides

Particle Swarm Optimization for Gravitational Wave Astronomy Yuta Michimura Department of

Ando Lab Seminar March 9, 2018 Particle Swarm Optimization for Gravitational Wave Astronomy Yuta Michimura Department of Physics, University of Tokyo Contents Background Review of optimization methods Review of PSO application to

654 views • 64 slides

Computational Emergence Research Area Richard P. Gabriel IBM Research 1 Ultra Large Scale

Computational Emergence Research Area Richard P. Gabriel IBM Research 1 Ultra Large Scale Systems diverse components ephemerality longevity continual change continual failure inconsistent beyond human comprehension 2 Prescription vs

550 views • 18 slides