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What is Hierarchical Clustering Algorithms

Handbook of Research on Machine Learning Innovations and Trends
A method to build a hierarchy of clusters either in agglomerative (each data item has its own cluster and then merge most similar clusters) or divisive (all data items belongs to the same cluster and this cluster will be divided recursively into smaller clusters).
Published in Chapter:
On Combining Nature-Inspired Algorithms for Data Clustering
Hanan Ahmed (Ain Shams University, Egypt), Howida A. Shedeed (Ain Shams University, Egypt), Safwat Hamad (Ain Shams University, Egypt), and Mohamed F. Tolba (Ain Shams University, Egypt)
Copyright: © 2017 |Pages: 30
DOI: 10.4018/978-1-5225-2229-4.ch036
This chapter proposed different hybrid clustering methods based on combining particle swarm optimization (PSO), gravitational search algorithm (GSA) and free parameters central force optimization (CFO) with each other and with the k-means algorithm. The proposed methods were applied on 5 real datasets from the university of California, Irvine (UCI) machine learning repository. Comparative analysis was done in terms of three measures; the sum of intra cluster distances, the running time and the distances between the clusters centroids. The initial population for the used algorithms were enhanced to minimize the sum of intra cluster distances. Experimental results show that, increasing the number of iterations doesn't have a noticeable impact on the sum of intra cluster distances while it has a negative impact on the running time. K-means combined with GSA (KM-GSA), PSO combined with GSA (PSO-GSA) gave the best performance according to the sum of intra cluster distances while K-means combined with PSO (KM-PSO) and KM-GSA were the best in terms of the running time. Finally, KM-GSA and GSA have the best performance.
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DSS Using Visualization of Multi-Algorithms Voting
Hierarchical clustering algorithms are clustering methods that classify datasets starting with all samples representing different clusters and gradually unite samples into clusters based on their likelihood measure.
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