This document describes an interactive genetic algorithm-based multi-objective approach to cluster gene expression patterns. The proposed Interactive Multi-Objective Clustering (IMOC) algorithm simultaneously evolves the set of validity measures to optimize and finds the clustering solution. It takes input from a human decision maker during execution to learn the best validity measures and clustering for the gene expression data. The algorithm is applied to benchmark gene expression datasets and shows more biologically significant clusters than other clustering algorithms.

1. AN INTERACTIVE APPROACHAN INTERACTIVE APPROACH
TO MULTIOBJECTIVETO MULTIOBJECTIVE
CLUSTERING OF GENECLUSTERING OF GENE
EXPRESSION PATTERNSEXPRESSION PATTERNS

2. Base Paper
An Interactive Approach toAn Interactive Approach to
Multiobjective Clustering of GeneMultiobjective Clustering of Gene
Expression PatternsExpression Patterns
Anirban Mukhopadhyay , Se nio r Me m be r, IEEE∗ , Ujjwal Maulik,
Se nio r Me m be r, IEEE, and Sanghamitra Bandyopadhyay, Se nio r
Me m be r, IEEE
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 60, NO. 1, JANUARY 2013

3. Abstract1

4. To find the best set of validity indices that should be optimized
simultaneously to obtain good clustering results.
In this project, a proposed interactive genetic algorithm-based multi
objective approach is used that simultaneously finds the clustering
solution as well as evolves the set of validity measures that are to be
optimized simultaneously.
The proposed method interactively takes the input from the human
decision maker during execution and adaptively learns from that input
to obtain the final set of validity measures along with the final
clustering result.
The algorithm is applied for clustering real-life benchmark gene
expression datasets and its performance is compared with that of
several other existing clustering algorithms to demonstrate its

5. Introduction2

6. Clustering is an important unsupervised data mining tool where a set of
patterns, usually vectors in multidimensional space, are grouped into K
clusters based on some similarity ordissimilarity criteria.
Data interdisciplinary subfield of computer science is the computational
process of discovering patterns in large data sets involving methods at
the intersection of artificial intelligence, machine learning, statistics,
and database systems.
The overall goal of the data mining process is to extract information
from a data set and transform it into an understandable structure for
furtheruse.
Aside from the raw analysis step, it involves database and data
management aspects, data pre-processing, model and inference
considerations, interestingness metrics, complexity considerations, post-

7. • Disadvantages
Existing System3

8. In the existing approaches of GA-based multi objective clustering, the
algorithms simultaneously optimize two or three chosen cluster
validity measures.
The algorithm uses the fuzzy c-means clustering control the similar
individuals gathered in a class and for each class construct non-
dominated set with arena's principle.
For fuzzy clustering of categorical data is proposed that encodes the
cluster modes and simultaneously optimizes fuzzy compactness and
fuzzy separation of the clusters.
The final clustering solution from the set of resultant Pareto-optimal
solution is involved. This is based on majority voting among Pareto
front solutions followed by k-nn classification.

9. DisadvantagesDisadvantages
It cannot be guaranteed that these predefined set of objective functions
forvalidity measures.
Point-based encoding techniques are straightforward, but sufferfrom
large chromosome lengths and hence slow rates of convergence.
Produce highly redundant chromosomes.

10. • Advantages
Proposed System4

11. The proposed interactive clustering algorithm is Interactive Multi
Objective Clustering.
The multi objective optimization problem has been modeled as a
minimization problemwhere all the objective functions are minimized.
The main NSGA-II procedure is modified to incorporate the interaction
with the DM in order to evolve the best set of objective functions as
well as the clustering simultaneously.
The final clustering solution has been obtained from the non-
dominated front produced in the final generation using support vector
machine classifierbased ensemble method.

12. AdvantagesAdvantages
The form of validity measures to be optimized simultaneously.
The most suitable subset of the validity measures forthe dataset.
Objective functions are more suitable forthe dataset.
Human decision maker.
Center-based encoding is that the chromosome length is shorter.
A fasterconvergence rate than point-based encoding techniques.

13. Hardware Requirements5

14. System : Dual Core
Processor
Hard Disk : 80 GB
Monitor : 15 VGA
color
Mouse : Logitech
RAM : 1 GB

15. Software Requirements6

16. Operating System : Windows XP.
Language : Java 7.
IDE : Net Beans
6.9.1.
Database : MySQL.

17. Modules7

18. • Pre-process
• Optimization
• Multiobjective Clustering
• Genetic Approach
• IMOC Algorithm

19. Results8

20. Main Screen

21. Load data into Database

22. Pre-process the data

23. Interactive DM

24. Cluster Centroids

25. Generated Heat Map

26. Performance Analysis of IMOC Algorithm

27. 9. Conclusion
 The performance of IMOC has been
demonstrated for two real-life gene
expression datasets and compared with that
of several other existing clustering
algorithms.
 Results indicate that IMOC produces more
biologically significant clusters compared to
the other algorithms and the better result
provided by IMOC is statistically significant.

28. References10

29. An Interactive Approach to Multiobjective Clustering of Gene Expression Patterns, Anirban
Mukhopadhyay∗, Senior Member, IEEE, Ujjwal Maulik, Senior Member, IEEE, and Sanghamitra
Bandyopadhyay, Senior Member, IEEE
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