Upload
luna-ashwini
View
228
Download
0
Embed Size (px)
Citation preview
7/31/2019 Variants in PSO
1/17
ASSOCIATION RULE MINING USING MEMETIC
ALGORITHM
Team : Ashwini V
Divyamary PRangalakshmi B
Sumithra M
Under the Guidance of
Dr. S. KanmaniProfessor
Dept. of IT
1
7/31/2019 Variants in PSO
2/17
Objective
To Propose an efficient methodology for mining
ARs using Memetic Algorithm
To compare the effectiveness of the algorithm
with Adaptive and Hybrid PSO methodologies
2
7/31/2019 Variants in PSO
3/17
Extraction of interesting information or patterns from
data in large databases is known as data mining.
Data Mining
3
7/31/2019 Variants in PSO
4/17
Association rule mining provides valuable information
in assessing significant correlations
It studies the frequency of items occurring together in
transactional databases
Relationship will be in the form of a rule: IF {X}, THEN {Y}
Parameters:
Support Level
Confidence Level
ASSOCIATION RULES
4
7/31/2019 Variants in PSO
5/17
55
DATASET DESCRIPTION
Dataset Name No. of
Instances
No. of
Attributes
Attribute
characteristics
Lenses 24 3 Categorical
HabermansSurvival
306 3 Integer
Car Evaluation 1728 6 Categorical
Post Operative
Patient
90 8 Categorical,
Integer
Zoo 101 17 Categorical,
Integer
7/31/2019 Variants in PSO
6/17
EXISTING SYSTEM
APRIORI ALGORITHM
Generate all frequent itemsets and gets all confident
association rules from those itemsets
FP GROWTH TREE
Stores information about frequent patterns in
the database
6
7/31/2019 Variants in PSO
7/17
CONT..
DYNAMIC ITEMSET COUNTING
o It is alternate to apriori itemset generation
o Considers only itemsets whose subsets are all frequent
7
7/31/2019 Variants in PSO
8/17
Most suitable in problems where multiple solutions are
required
GENETIC ALGORITHM is a global optimization method inspired
by biological mechanisms such as evolution and hereditary
Parallel implementation is easier
EVOLUTIONARY ALGORITHM
8
7/31/2019 Variants in PSO
9/17
PARTICLE SWARM OPTIMIZATION
Particle swarm optimization is a population based
optimization Algorithm
Aims to simulate social behaviors in nature found in
insects, birds, fish, etc
Each data itemset are represented as particles
Driven by both personal and social influences
9
7/31/2019 Variants in PSO
10/17
Pitfalls in Traditional PSO
The standard PSO algorithm can easily get trapped in
the local optima when solving complex multimodal
problems
Becomes computationally inefficient as it depends on
the function evaluators (FEs) required
10
7/31/2019 Variants in PSO
11/17
PSO
Memetic
APSO
Hybrid
(PSO+DE)
(PSO+QC)
PROPOSED SYSTEM
11
7/31/2019 Variants in PSO
12/17
12
Memetic Particle Swarm Optimization scheme incorporates
local search techniques in the standard Particle Swarm
Optimization algorithm, resulting in an efficient and effective
optimization method.
Two local searches
Solis and Wets local search strategy
Shuffled Frog Leaping Algorithm based search strategy
Memetic PSO
7/31/2019 Variants in PSO
13/17
Adaptive PSO
Adaptive particle swarm optimization (APSO) perform global
search over the entire search space
Identifies one of the following four evolutionary states:
exploration, exploitation, convergence, and jumping outin
each generation
Enable the automatic control ofinertia weight, acceleration
coefficients, and other algorithmic parameters at run time
13
7/31/2019 Variants in PSO
14/17
Hybrid
(PSO+DE) DEPSO is a strategy of Dual Evolution (DES) based on the
mechanism for sharing information.
Improves the drawbacks easy to drop into region
optimum and increases the performance with stableconvergence.
(PSO+QC)
The quantum theory of mechanics governs the
movement of swarm particles along with an interpolationbased recombination operator.
14
7/31/2019 Variants in PSO
15/17
MONTH Rangalakshmi B
Sumithra M
Ashwini V
Divya Mary P
August -
September
Self Adaption of
control parameters
Memetic algorithm
(PSO + Local search)
October -
November
Hybrid model 1
(PSO + DE)
Hybrid model 2
(PSO + QC)
December Comparison of results and further Enhancement
WORK PLAN
15
7/31/2019 Variants in PSO
16/17
BASE PAPERS
Jiuzhong Zhang , XuemingDing.: A Multi-Swarm Self-Adaptive
and Cooperative Particle Swarm Optimization, Engineering
Applications of Artificial Intelligence, Volume 24,pp. 958967,
2011.
Zhan, Z-H. and Zhang, J. and Li, Y. and Chung, H.S-H. (2009)
Adaptive particle swarm optimization. IEEE Transactions on
Systems Man, and Cybernetics Part B: Cybernetics, 39 (6).
pp. 1362-1381.
16
7/31/2019 Variants in PSO
17/17
REFERENCES
Nickabadi,A. Ebadzadeh, M M. and Safabakhsh, R. A novel
particle swarm optimization algorithm with adaptive inertia
weight.Applied Soft Computing, 11,36583670, 2011.
Radha Thangaraj , Millie Pant , Ajith Abraham , Pascal Bouvry
.: Particle swarm optimization: Hybridization perspectives
and experimental illustrations, Applied Mathematics and
Computation 217 (2011) 52085226.
17