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Transcript of Swarm Intelligence In
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Frequent Pattern Mining using
Evolutionary Techniques
Presented by
Maryam Zardad
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Contents
Introduction
Swarm Intelligence
Ant Colony Algorithm Bee Algorithm
Genetic Algorithm
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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)
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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).
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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.
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(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).
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Thanks