### Abstract

Clustering methods divide the dataset into groups called clusters such that the objects in the same cluster are more similar and objects in the different clusters are dissimilar. Clustering algorithms can be hierarchical or partitional. Partitional clustering methods decompose the dataset into set of disjoint clusters. Most partitional approaches assume that the number of clusters are known a priori. Moreover, they are sensitive to initialization. Hierarchical clustering methods produce a complete sequence of clustering solutions, either from singleton clusters to a cluster including all individuals or vice versa. Hierarchical clustering can be represented by help of a dendrogram that can be cut at different levels to obtain different number of clusters of corresponding granularities. If dataset has large multilevel hierarchies then it becomes difficult to determine optimal clustering by cutting the dendrogram at every level and validating clusters obtained for each level. Genetic Algorithms (GAs) have proven to be a promising technique for solving complex optimization problems. In this paper, we propose an Optimal Clustering Genetic Algorithm (OCGA) to find optimal number of clusters. The proposed method has been applied on some artificially generated datasets. It has been observed that it took less number of iterations of cluster validation to arrive at optimal number of clusters.

Original language | English |
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Title of host publication | Proceedings of the International Conference on Soft Computing for Problem Solving, SocProS 2011 |

Pages | 295-303 |

Number of pages | 9 |

Edition | VOL. 2 |

DOIs | |

Publication status | Published - 23-05-2012 |

Externally published | Yes |

Event | International Conference on Soft Computing for Problem Solving, SocProS 2011 - Roorkee, India Duration: 20-12-2011 → 22-12-2011 |

### Publication series

Name | Advances in Intelligent and Soft Computing |
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Number | VOL. 2 |

Volume | 131 AISC |

ISSN (Print) | 1867-5662 |

### Conference

Conference | International Conference on Soft Computing for Problem Solving, SocProS 2011 |
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Country | India |

City | Roorkee |

Period | 20-12-11 → 22-12-11 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Computer Science(all)

### Cite this

*Proceedings of the International Conference on Soft Computing for Problem Solving, SocProS 2011*(VOL. 2 ed., pp. 295-303). (Advances in Intelligent and Soft Computing; Vol. 131 AISC, No. VOL. 2). https://doi.org/10.1007/978-81-322-0491-6_29

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*Proceedings of the International Conference on Soft Computing for Problem Solving, SocProS 2011.*VOL. 2 edn, Advances in Intelligent and Soft Computing, no. VOL. 2, vol. 131 AISC, pp. 295-303, International Conference on Soft Computing for Problem Solving, SocProS 2011, Roorkee, India, 20-12-11. https://doi.org/10.1007/978-81-322-0491-6_29

**Optimal clustering method based on genetic algorithm.** / Gajawada, Satish; Toshniwal, Durga; Patil, Nagamma; Garg, Kumkum.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Optimal clustering method based on genetic algorithm

AU - Gajawada, Satish

AU - Toshniwal, Durga

AU - Patil, Nagamma

AU - Garg, Kumkum

PY - 2012/5/23

Y1 - 2012/5/23

N2 - Clustering methods divide the dataset into groups called clusters such that the objects in the same cluster are more similar and objects in the different clusters are dissimilar. Clustering algorithms can be hierarchical or partitional. Partitional clustering methods decompose the dataset into set of disjoint clusters. Most partitional approaches assume that the number of clusters are known a priori. Moreover, they are sensitive to initialization. Hierarchical clustering methods produce a complete sequence of clustering solutions, either from singleton clusters to a cluster including all individuals or vice versa. Hierarchical clustering can be represented by help of a dendrogram that can be cut at different levels to obtain different number of clusters of corresponding granularities. If dataset has large multilevel hierarchies then it becomes difficult to determine optimal clustering by cutting the dendrogram at every level and validating clusters obtained for each level. Genetic Algorithms (GAs) have proven to be a promising technique for solving complex optimization problems. In this paper, we propose an Optimal Clustering Genetic Algorithm (OCGA) to find optimal number of clusters. The proposed method has been applied on some artificially generated datasets. It has been observed that it took less number of iterations of cluster validation to arrive at optimal number of clusters.

AB - Clustering methods divide the dataset into groups called clusters such that the objects in the same cluster are more similar and objects in the different clusters are dissimilar. Clustering algorithms can be hierarchical or partitional. Partitional clustering methods decompose the dataset into set of disjoint clusters. Most partitional approaches assume that the number of clusters are known a priori. Moreover, they are sensitive to initialization. Hierarchical clustering methods produce a complete sequence of clustering solutions, either from singleton clusters to a cluster including all individuals or vice versa. Hierarchical clustering can be represented by help of a dendrogram that can be cut at different levels to obtain different number of clusters of corresponding granularities. If dataset has large multilevel hierarchies then it becomes difficult to determine optimal clustering by cutting the dendrogram at every level and validating clusters obtained for each level. Genetic Algorithms (GAs) have proven to be a promising technique for solving complex optimization problems. In this paper, we propose an Optimal Clustering Genetic Algorithm (OCGA) to find optimal number of clusters. The proposed method has been applied on some artificially generated datasets. It has been observed that it took less number of iterations of cluster validation to arrive at optimal number of clusters.

UR - http://www.scopus.com/inward/record.url?scp=84861218903&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84861218903&partnerID=8YFLogxK

U2 - 10.1007/978-81-322-0491-6_29

DO - 10.1007/978-81-322-0491-6_29

M3 - Conference contribution

AN - SCOPUS:84861218903

SN - 9788132204909

T3 - Advances in Intelligent and Soft Computing

SP - 295

EP - 303

BT - Proceedings of the International Conference on Soft Computing for Problem Solving, SocProS 2011

ER -