swarm intelligence in

7/31/2019 Swarm Intelligence In

1/30

Frequent Pattern Mining using

Evolutionary Techniques

Presented by

Maryam Zardad


2/30

Contents

Introduction

Swarm Intelligence

Ant Colony Algorithm Bee Algorithm

Genetic Algorithm


3/30

Frequent Pattern Mining

Frequent pattern mining is an important area

of Data mining.

The frequent patterns are patterns (such as

itemsets, subsequences) that appear in a dataset frequently.


4/30

For example, a set of items, such as milk and

bread that appear frequently together in a

transaction data set is afrequent itemset. A

subsequence, such as buying first a PC, then a

digital camera, and then a memory card, if it

occurs frequently in a shopping history

database, is a (frequent) sequential pattern.


5/30

Association Rule Mining

Association rule mining (ARM) is one of the

core data mining techniques. The major aim of

ARM is to extract rules on how a subset of

items influences the presence of another

subset.


6/30

Swarm Intelligence

A branch of nature inspired algorithms whichare called as swarm intelligence is focused oninsect behavior.

Interaction between insects contributes to thecollective intelligence of the social insectcolonies.

Ant Colonies (AC) are currently the mostpopular algorithms in the swarm intelligencedomain.


7/30

Ant Colony

Ant Colonies Optimization (ACO) algorithms

were introduced around 1990 . These

algorithms were inspired by the behavior of

ant colonies. Ants are social insects, beinginterested mainly in the colony survival rather

than individual survival.


8/30

When searching for food, ants initially explore

the area surrounding their nest in a random

manner. While moving, ants leave a chemical

pheromone trail on the ground.


9/30

Ants are guided by pheromone smell. Ants

tend to choose the paths marked by the

strongest pheromone concentration . When

an ant finds a path, it evaluates the quantity

and the quality of the food by pheromone.


10/30


11/30

During the return trip, the quantity of

pheromone that an ant leaves on the groundmay depend on the quantity and quality of

the food. The pheromone trails will guide

other ants to the food source.


12/30

Types of pheromone

There are generally two types of pheromone

1. Food pheromone

2. Nest pheromone

While ant is looking for food it drops nest

pheromone and when it finds food it drops foodpheromone.


13/30

The main steps of the ACO algorithm are given below:

1. Wander randomly, in general direction of any nearbypheromones.

2. If the ant is holding food, drop food pheromonewhile looking for and following a nest pheromone

that leads in the general direction of nest. If the ant isnot holding food, drop nest pheromone while lookingfor and following a food pheromone trail.

3. If the ant finds itself at food and is not holding any,

pick the food up.4. If the ant finds itself at the nest and is carrying food,

drop the food.


14/30

Ant Colony Optimization and Data

mining

Ant colony based clustering algorithms have been

first introduced by Deneubourg et al. by

mimicking different types of naturally-occurring

emergent phenomena. Ants gather items to formheaps (clustering of dead corpses or cemeteries)

Ramos et al. proposedACLUSTER algorithm to

follow real ant-like behaviors as much as possible. Abraham and Ramos proposed an ant clustering

algorithm to discover Web usage patterns.


15/30

The Bees Algorithm (BA)

Bees in nature

A colony of honey bees can extend itself

over long distances in multiple directions

(more than 10 km)


16/30

Waggle dance of bees

By performing this dance, successful foragers

share the information about the direction and

distance to patches of flower and the amount

of nectar within this flower with their hivemates. So this is a successful mechanism

which foragers can recruit other bees in their

colony to productive locations to collectvarious resources.


17/30

while performing the waggle dance, the

direction of bees indicates the direction of the

food source in relation to the Sun, the

intensity of the waggles indicates how far

away it is and the duration of the dance

indicates the amount of nectar on relatedfood source.


18/30


19/30

2. Recruit: If the unemployed forager attends

to a waggle dance done by some other bee,

the bee will start searching by using the

knowledge from waggle dance.


20/30

Employed foragers :

When the recruit bee finds the food source, it

will raise to be an employed forager who

memorizes the location of the food source.

After the employed foraging bee loads a

portion of nectar from the food source, it

returns to the hive and unloads the nectar to

the food area in the hive.


21/30

There are three possible options related to

residual amount of nectar for the foraging

bee.

1. If the nectar amount decreased to a low

level or exhausted, foraging bee abandons

the food source and become an unemployed

bee.


22/30

2. If there are still sufficient amount of nectar in

the food source, it can continue to forage

without sharing the food source information

with the nest mates

3. Or it can go to the dance area to perform

waggle dance for informing the nest mates

about the same food source. The probability

values for these options highly related to the

quality of the food source.


23/30

Bee Algorithm

1. Initialise population with random solutions.2. Evaluate fitness of the population.

3. While (stopping criterion not met)

//Forming new population.

4. Select sites for neighbourhood search.

5. Recruit bees for selected sites and evaluate fitnesses.

6. Select the fittest bee from each patch.

7. Assign remaining bees to search randomlyand evaluate their fitnesses.

8. End While.


24/30

Genetic Algorithm

GAs are one of the best ways to solve a

problem for which little is known.

Standard GA apply genetic operators such

selection, crossoverand mutation on an

initially random population in order to

compute a whole generation of new strings.

The process is terminated when an acceptable

or optimum solution is found.


25/30

The functions of genetic operators are as

follows:

1) Selection: Selection deals with the

probabilistic survival of the fittest, in that, more

fit chromosomes are chosen to survive. Where

fitness is a comparable measure of how well a

chromosome solves the problem at hand.


26/30


27/30

3) Mutation: Alters the new solutions so as to

add in the search for better solutions. This is

the chance that a bit within a chromosome

will be flipped (0 becomes 1, 1 becomes 0).


28/30

The genetic algorithm based method for

finding frequent itemsets repeatedly

transforms the population by executing thefollowing steps:

(1) Fitness Evaluation: The fitness (i.e., an

objective function) is calculated for eachindividual.

(2) Selection: Individuals are chosen from the

current population as parents to be involved inrecombination.


29/30

(3) Recombination: New individuals (called

offspring) are produced from the parents by

applying genetic operators such as crossover

and mutation.

(4) Replacement: Some of the offspring are

replaced with some individuals (usually with

their parents).


30/30

Thanks

swarm intelligence in

Documents