Data Clustering Using Swarm Intelligence Algorithms

An Overview

Faculty of Computers and Information, Cairo University and SRGE member

Mona M.Soliman

http://www.egyptscience.net

Bio-inspiring and evolutionary computation: Trends, applications and open issues workshop, 7 Nov. 2015 Faculty of Computers and Information, Cairo University

Agenda

Introduction Types of data clustering Classical clustering

Algorithms Swarm Intelligence

Algorithms Clustering with SI Algorithms

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Clustering means the act of partitioning an

unlabeled dataset into groups of similar objects. Each group, called a `cluster', consists of

objects that are similar between themselves and dissimilar to objects of other groups.

From a machine learning perspective, clusters correspond to the hidden patterns in data, the search for clusters is a kind of unsupervised learning, and the resulting system represents a data concept.

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IntroductionProblem Definition

In the past few decades, cluster analysis has played a central role in a variety of fields ranging from : Engineering (machine learning, artificial intelligence, pattern

recognition, mechanical engineering, electrical engineering) Computer sciences (web mining, spatial database analysis, textual

document collection, image segmentation) Life and medical sciences (genetics, biology,

microbiology,paleontology, psychiatry, pathology) Earth sciences (geography. geology, remote sensing) Social sciences (sociology, psychology, archeology, education) Economics (marketing, business)

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IntroductionMotivation

What is a good cluster is5

Inter-cluster distances are maximized

Intra-cluster distances are

minimized

Types of Data Clustering6

Data Clustering

Hierarchal

Agglomerative Divisive

PartitionalErro

r Minimization

Graph

theoretic

Density Based

Model

Based

bottom-up

Top-down

K means

minimal Spanning Tree

expectation

maximation

-Decision tree

-Neural network

Hierarchal clustering7

Types of Data Clustering8

Data Clustering

Hierarchal

Agglomerative Divisive

PartitionalErro

r Minimization

Graph

theoretic

Density Based

Model

Based

bottom-up

Top-down

K means

minimal Spanning Tree

expectation

maximation

-Decision tree

-Neural network

Original Points A Partitional Clustering

Partitinal clustering

The Classical Clustering Algorithmsk-means Algorithm The K-means algorithm

groups D-dimensional data vectors into a predefined number of clusters on the basis of the Euclidean distance as the similarity criteria.

Euclidean distances among data vectors are minimum for data vectors within a cluster as compared with distances to other data vectors in different clusters.

Vectors of the same cluster are associated with one centroid vector, which represents the center of that cluster and is the mean of the data vectors that belong together.

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Swarm Intelligence AlgorithmsBiological Foundation

The collective and social behavior of living creatures motivated researchers to undertake the study of today what is known as Swarm Intelligence

The efforts to mimic such behaviors through computer simulation finally resulted into the fascinating field of SI.

SI systems are typically made up of a population of simple agents interacting locally with one another and with their environment.

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The behavior of a single ant, bee, termite and wasp often is too simple, but their collective and social behavior is of paramount significance.

Ant Colony Optimization (1992)

Particle Swarm Optimization (1995)

Fish Swarm Optimization (2002)

Bee Swarm Optimization (2005)

Cat Swarm Optimization (2006)

Firefly Optimization (2008) Cuckoo Search Optimization

(2009) Bat Swarm Optimization

(2010) Grey wolf Optimization (2014)

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Swarm Intelligence AlgorithmsAn overview

Clustering with the SI AlgorithmsRelevance of SI Algorithms in Clustering

Data clustering may be well formulated as a difficult global optimization problem; thereby making the application of SI tools more obvious and appropriate.

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Improving the performance of existing classical clustering methods (e.g. k-means , k-medoid, fuzzy clustering )• K-means clustering have many drawbacks: Such as being

trapped in local minimum and being sensitive to initial cluster centers

• improve the cluster quality by refinement algorithm. (ACO,PSO,Bee Swarm, Firefly Swarm)• Determining the optimal number of clusters• Determine the initial cluster centers

Clustering with the SI AlgorithmsRelevance of SI Algorithms in Clustering

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Clustering with the SI AlgorithmsRelevance of SI Algorithms in Clustering

Creation of clustering algorithm based on SI algorithms• Fish swarm Clustering• Cat Swarm Clustering

Thank You