SlideShare a Scribd company logo

Multi-objective Evolutionary Clustering : A survey

A
Aiswarya Issac

The document discusses multiobjective evolutionary clustering. It begins by defining clustering and multi-objective clustering. Multiobjective clustering aims to decompose a data set into similar groups by maximizing multiple objectives simultaneously. Evolutionary algorithms are applied to solve multiobjective clustering problems. The process involves representing clustering solutions in chromosomes, initializing a population, selecting objectives, applying operations like selection and mutation, and obtaining a Pareto optimal set of solutions.

Technology
Related topics:
Computer Science Basics
1 of 53
Downloaded 22 times
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Multiobjective Evolutionary Clustering Aiswarya Issac 27 January, 2016 Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 1 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Overview Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 2 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Clustering Partitioning into homogeneous groups based on some similarity metric. Click Here Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 3 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Outline Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 4 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Optimization Problem Single Objective Optimization Only one objective function to be minimized. Eg. Knapsack problem Multiple Objective Optimization Two or more conflicting objectives need to be optimized. There will be a set of possible solutions rather than a single optimal solution - Pareto optimal solutions. Eg. Minimizing cost while maximizing comfort while buying a car. Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 5 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Optimization Problem Figure: 1 Illustration of knapsack problem[Source:wikipedia] Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 6 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Outline Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 7 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Multiobjective clustering Single Objective Clustering Figure: 2 Comparison of different clustering[1] The final clusters do not represent a global optimization result. Different final clustering can happen based on the initial selection of cluster center. Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 8 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Multiobjective Clustering Multi Objective Clustering Decompose a data-set into similar groups maximizing multiple objectives in parallel. Figure: 3 Output for multiobjective clustering[1] Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 9 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Evolutionary Algorithms Process of Evolution Figure: 4 Schematic representation of evolutionary algorithm[2] Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 10 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Evolutionary Algorithms Process of Evolution Figure: 5 Schematic representation datastructures[2] Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 11 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Outline Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 12 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Evolutionary Algorithms Why Evolutionary Method? [3] Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 13 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Evolutionary Algorithms Why Evolutionary Method? [3] Antennas developed by NASA’s Evolvable Systems Group for use on satellites. Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 13 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Evolutionary Algorithms Why Evolutionary Method? [3] Antennas developed by NASA’s Evolvable Systems Group for use on satellites. Free of any human preconceptions or biases Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 13 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Evolutionary Algorithms Multiobjective Clustering Steps[3] Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 14 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Evolutionary Algorithms Multiobjective Clustering Steps[3] Choose a possible encoding of chromosome to represent a clustering solution. Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 14 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Evolutionary Algorithms Multiobjective Clustering Steps[3] Choose a possible encoding of chromosome to represent a clustering solution. Generate the initial population of chromosomes. Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 14 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Evolutionary Algorithms Multiobjective Clustering Steps[3] Choose a possible encoding of chromosome to represent a clustering solution. Generate the initial population of chromosomes. Choose a suitable set of objective functions that are to be optimized simultaneously. Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 14 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Evolutionary Algorithms Multiobjective Clustering Steps[3] Choose a possible encoding of chromosome to represent a clustering solution. Generate the initial population of chromosomes. Choose a suitable set of objective functions that are to be optimized simultaneously. Design suitable evolutionary operators such as selection, crossover, and mutation. Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 14 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Evolutionary Algorithms Multiobjective Clustering Steps[3] Choose a possible encoding of chromosome to represent a clustering solution. Generate the initial population of chromosomes. Choose a suitable set of objective functions that are to be optimized simultaneously. Design suitable evolutionary operators such as selection, crossover, and mutation. Define a fitness function to evaluate the clustering solutions. Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 14 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Evolutionary Algorithms Multiobjective Clustering Steps[3] Choose a possible encoding of chromosome to represent a clustering solution. Generate the initial population of chromosomes. Choose a suitable set of objective functions that are to be optimized simultaneously. Design suitable evolutionary operators such as selection, crossover, and mutation. Define a fitness function to evaluate the clustering solutions. Develop a technique to obtain a single clustering solution from Pareto optimal set. Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 14 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Outline Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 15 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Solution Representation Figure: 6 Classification of solution representation[4] Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 16 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Solution Representation Prototype based Centroid-based: The coordinates of the cluster centers will be encoded in the chromosome. Eg: [2.4 5.9, 0.36 2.7, 5.3 10.2] Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 17 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Solution Representation Prototype based Centroid-based: The coordinates of the cluster centers will be encoded in the chromosome. Eg: [2.4 5.9, 0.36 2.7, 5.3 10.2] Medoid-based: Used when coordinates are not known. An actual point that is most centrally located in a cluster is used to represent that cluster. Eg. datapoints = [1,2,3,4,5,6,7,8] encoding = [3,7] Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 17 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Solution Representation Prototype based Centroid-based: The coordinates of the cluster centers will be encoded in the chromosome. Eg: [2.4 5.9, 0.36 2.7, 5.3 10.2] Medoid-based: Used when coordinates are not known. An actual point that is most centrally located in a cluster is used to represent that cluster. Eg. datapoints = [1,2,3,4,5,6,7,8] encoding = [3,7] Mode-based: Similar to medoid based Computation is less expensive when compared with medoid based. Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 17 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Solution Representation Point based Cluster Label-based: Figure: 7 Cluster Label based encoding scheme[4] Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 18 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Solution Representation Point based Cluster Label-based: Figure: 7 Cluster Label based encoding scheme[4] Locus-based Adjacency Graph: Figure: 8 Locus based encoding scheme[4] Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 18 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Outline Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 19 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Initializing Population Prototype-based encoding The prototypes in the initial population are usually some randomly selected data points. Point based encoding The cluster labels will be initialized with random strings so that each point gets a random cluster label. Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 20 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Outline Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 21 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Objective functions Overall Cluster Deviation Cluster Connectedness Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 22 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Outline Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 23 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Operations Selection[5] Selection is based on fitness function. Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 24 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Operations Selection[5] Selection is based on fitness function. Tournament selection. Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 24 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Operations Selection[5] Selection is based on fitness function. Tournament selection. Ranking Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 24 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Operations Selection[5] Selection is based on fitness function. Tournament selection. Ranking Proportionate Selection Figure: 9 Roulette wheel selection[2] Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 24 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Operations Crossover Figure: 10 Classification of crossover schemes[4] Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 25 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Operations Crossover Single or multiple point crossover For prototype based: Parent1: [2.4 5.9, 0.36 2.7, 5.3 10.2] Parent2: [2.5 5.5, 1.2 2.3, 6.0 10.2] Offspring1: [2.4 5.9, 1.2 2.3, 6.0 10.2] Offspring2: [2.5 5.5, 0.36 2.7, 5.3 10.2] Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 26 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Operations Crossover Single or multiple point crossover For prototype based: Parent1: [2.4 5.9, 0.36 2.7, 5.3 10.2] Parent2: [2.5 5.5, 1.2 2.3, 6.0 10.2] Offspring1: [2.4 5.9, 1.2 2.3, 6.0 10.2] Offspring2: [2.5 5.5, 0.36 2.7, 5.3 10.2] For point based: Uniform crossover approach Parent1: [1 1 1 2 3 3 2 3 ] Parent2: [1 1 2 2 2 3 3 2 ] Mask : [0 0 1 1 1 1 0 1 ] Offspring1: [1 1 2 2 2 3 2 2 ] Offspring2: [1 1 1 2 3 3 3 3 ] Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 26 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Operations Mutation Figure: 11 Classification of mutation schemes[4] Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 27 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Operations Mutation For prototype based encoding: The cluster center is modified as follows: zkl = (1 ± 2ε)zkl Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 28 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Operations Mutation For prototype based encoding: The cluster center is modified as follows: zkl = (1 ± 2ε)zkl For point based encoding: A point is chosen with probability 1/n. Its cluster label is randomly mutated, along with predefined number of neighbours. Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 28 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Outline Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 29 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Final Solution Selection Figure: 12 Classification of approaches for final solutions[4] Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 30 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Final Solution Selection Independent Objective-based: Objective functions are used to evaluate the best solution. Knee-based: Knee solution is one for which change in one objective value induces maximum change in others. Cluster Ensemble-based: If some points are always clustered together by a majority of the solutions, then these points may be assumed to be clustered appropriately. So, this can be used to train a supervised classifier. Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 31 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Outline Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 32 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Applications Applications Tasks Bioinformatics Grouping co-expressed genes Clustering samples Protein complex identification Social Network Analytics Social network clustering Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 33 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Different Algorithms Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 34 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Clustering Multi-objective clustering Optimization Problems Multiobjective Clustering Evolutionary Algorithms Multiobjective Evolutionary Clustering Evolutionary Method Solution Representation Initializing Population Selection of Objective functions Operations Final Solution Selection Applications Conclusion Conclusion Evolutionary Algorithms can be used to obtain solutions for unconventional problems like multiobjective clustering. Suggestions: Chromosome Encoding: Fast decoding and small length. Initialization: Pre-processing of input. Final solution selection: Use multiple objectives together. Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 35 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Appendix For Further Reading Reference I [1] Martin H. C. Law, Alexander P. Topchy, Anil K. Jain, ’Multiobjective Data Clustering’, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. [2] Carlos A , Gary B and David A, Chapter 1 and 2, in ’Evolutionary Algorithms for solving Multiobjective problem’., 2nd ed, Springer, 2007. [3] Daniel W. Dyer, ’The power of evolution’, in ’Evolutionary Computation in Java’, ’http://watchmaker.uncommons.org/manual/index.html’, 2010 Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 36 / 37
Multiobjective Evolutionary Clustering Aiswarya Issac Appendix For Further Reading Reference II [4] Anirban Mukhopadhyay, Ujjwal Maulik, and Sanghamitra Bandyopadhyay. 2015. ’A survey of multiobjective evolutionary clustering’. ACM Comput. Surv. 47, 4, Article 61 (May 2015). [5] Abdullah Konak, David W. Coit, Alice E. Smith, Multi-objective optimization using genetic algorithms: A tutorial’. Reliability Engineering and System Safety,Elsevier,2006 Aiswarya Issac Multiobjective Evolutionary Clustering 27 January, 2016 37 / 37

More Related Content

PDF
Bachelor_Defense
Teja Turk
 
PPT
An interactive approach to multiobjective clustering of gene expression patterns
Ravi Kumar
 
PPT
Fuzzy dm
Akshay Chaudhari
 
PPT
Leach & Pegasis
ReenaShekar
 
PPT
Fuzzy c-means clustering for image segmentation
Dharmesh Patel
 
PPTX
Cuckoo Search & Firefly Algorithms
Mustafa Salam
 
PPTX
Firefly algorithm
Hasan Gök
 
PPT
Wireless Sensor Network
Shubhi Singh chauhan
 
Bachelor_Defense
Teja Turk
 
An interactive approach to multiobjective clustering of gene expression patterns
Ravi Kumar
 
Fuzzy dm
Akshay Chaudhari
 
Leach & Pegasis
ReenaShekar
 
Fuzzy c-means clustering for image segmentation
Dharmesh Patel
 
Cuckoo Search & Firefly Algorithms
Mustafa Salam
 
Firefly algorithm
Hasan Gök
 
Wireless Sensor Network
Shubhi Singh chauhan
 

Similar to Multi-objective Evolutionary Clustering : A survey (15)

PDF
Automatic Unsupervised Data Classification Using Jaya Evolutionary Algorithm
aciijournal
 
PPTX
Evaluating Machine Learning Approaches to Classify Pharmacy Students’ Reflect...
mingliu107
 
PDF
EJSR(5)
AMIT KUMAR
 
PPTX
Ensemble learning
Mustafa Sherazi
 
PPTX
Kernel based swarm optimization for renewable energy application
Aboul Ella Hassanien
 
PPTX
Ensemble Method.pptx
yashaswinitiwari1
 
PPTX
Ensemble Learning.pptx machine learning1
pashamshashanthraopa
 
PDF
Automatic Unsupervised Data Classification Using Jaya Evolutionary Algorithm
aciijournal
 
PDF
WEIGHTED CONSTRAINT SATISFACTION AND GENETIC ALGORITHM TO SOLVE THE VIEW SELE...
IJDMS
 
PPTX
Biomedical Word Sense Disambiguation presentation [Autosaved]
akm sabbir
 
PDF
A Value Based Approach to Co-Designing Symbiotic Product-Service System - Yoo...
ServDes
 
PPTX
Multiple Classifier Systems
Farzad Vasheghani Farahani
 
PDF
Ase12.ppt
Yann-Gaël Guéhéneuc
 
PDF
Ant System and Weighted Voting Method for Multiple Classifier Systems
IJECEIAES
 
PDF
Eucalyptus Release of Open edX
Appsembler
 
Automatic Unsupervised Data Classification Using Jaya Evolutionary Algorithm
aciijournal
 
Evaluating Machine Learning Approaches to Classify Pharmacy Students’ Reflect...
mingliu107
 
EJSR(5)
AMIT KUMAR
 
Ensemble learning
Mustafa Sherazi
 
Kernel based swarm optimization for renewable energy application
Aboul Ella Hassanien
 
Ensemble Method.pptx
yashaswinitiwari1
 
Ensemble Learning.pptx machine learning1
pashamshashanthraopa
 
Automatic Unsupervised Data Classification Using Jaya Evolutionary Algorithm
aciijournal
 
WEIGHTED CONSTRAINT SATISFACTION AND GENETIC ALGORITHM TO SOLVE THE VIEW SELE...
IJDMS
 
Biomedical Word Sense Disambiguation presentation [Autosaved]
akm sabbir
 
A Value Based Approach to Co-Designing Symbiotic Product-Service System - Yoo...
ServDes
 
Multiple Classifier Systems
Farzad Vasheghani Farahani
 
Ase12.ppt
Yann-Gaël Guéhéneuc
 
Ant System and Weighted Voting Method for Multiple Classifier Systems
IJECEIAES
 
Eucalyptus Release of Open edX
Appsembler
 
Ad

Recently uploaded (20)

PDF
Electronic commerce courselecture one. Pdf
OmerMohamed64
 
PDF
7 ChatGPT Prompts to Help You Define Your Ideal Customer Profile.pdf
SOFTTECHHUB
 
PDF
Per capita expenditure prediction using model stacking based on satellite ima...
IAESIJAI
 
PDF
Spectral efficient network and resource selection model in 5G networks
IAESIJAI
 
PDF
Dropbox Q2 2025 Financial Results & Investor Presentation
Dropbox
 
PDF
Chapter 3 Spatial Domain Image Processing.pdf
Getnet Tigabie Askale -(GM)
 
PPTX
Detection-First SIEM: Rule Types, Dashboards, and Threat-Informed Strategy
ReZa AdineH
 
PDF
MIND Revenue Release Quarter 2 2025 Press Release
MIND CTI
 
PDF
Mobile App Security Testing_ A Comprehensive Guide.pdf
flufftailshop
 
PDF
Encapsulation theory and applications.pdf
gurumoop
 
PDF
Review of recent advances in non-invasive hemoglobin estimation
IAESIJAI
 
PDF
Unlocking AI with Model Context Protocol (MCP)
Brian McKeiver
 
PDF
cuic standard and advanced reporting.pdf
SimoneTitaniumTomase
 
PDF
Building Integrated photovoltaic BIPV_UPV.pdf
SandeepSingh286037
 
PDF
How UI/UX Design Impacts User Retention in Mobile Apps.pdf
SynapseIndia
 
PDF
KodekX | Application Modernization Development
KodekX
 
PDF
Diabetes mellitus diagnosis method based random forest with bat algorithm
IAESIJAI
 
PDF
Network Security Unit 5.pdf for BCA BBA.
Serpent6
 
PPTX
20250228 LYD VKU AI Blended-Learning.pptx
DatVoNgoc
 
PDF
The Rise and Fall of 3GPP – Time for a Sabbatical?
3G4G
 
Electronic commerce courselecture one. Pdf
OmerMohamed64
 
7 ChatGPT Prompts to Help You Define Your Ideal Customer Profile.pdf
SOFTTECHHUB
 
Per capita expenditure prediction using model stacking based on satellite ima...
IAESIJAI
 
Spectral efficient network and resource selection model in 5G networks
IAESIJAI
 
Dropbox Q2 2025 Financial Results & Investor Presentation
Dropbox
 
Chapter 3 Spatial Domain Image Processing.pdf
Getnet Tigabie Askale -(GM)
 
Detection-First SIEM: Rule Types, Dashboards, and Threat-Informed Strategy
ReZa AdineH
 
MIND Revenue Release Quarter 2 2025 Press Release
MIND CTI
 
Mobile App Security Testing_ A Comprehensive Guide.pdf
flufftailshop
 
Encapsulation theory and applications.pdf
gurumoop
 
Review of recent advances in non-invasive hemoglobin estimation
IAESIJAI
 
Unlocking AI with Model Context Protocol (MCP)
Brian McKeiver
 
cuic standard and advanced reporting.pdf
SimoneTitaniumTomase
 
Building Integrated photovoltaic BIPV_UPV.pdf
SandeepSingh286037
 
How UI/UX Design Impacts User Retention in Mobile Apps.pdf
SynapseIndia
 
KodekX | Application Modernization Development
KodekX
 
Diabetes mellitus diagnosis method based random forest with bat algorithm
IAESIJAI
 
Network Security Unit 5.pdf for BCA BBA.
Serpent6
 
20250228 LYD VKU AI Blended-Learning.pptx
DatVoNgoc
 
The Rise and Fall of 3GPP – Time for a Sabbatical?
3G4G
 
Ad

Multi-objective Evolutionary Clustering : A survey