A Hybrid Approach for Data Clustering using Expectation-Maximization and Parameter Adaptive Harmony Search Algorithm

AbstractIn this paper, the problem of automatic data clustering is treated as the searching of optimal number of clusters so that the obtained partitions should be optimized. The automatic data clustering technique utilizes a recently developed parameter adaptive harmony search (PAHS) as an underlying optimization strategy. It uses real-coded variable length harmony vector, which is able to detect the number of clusters automatically. The newly developed concepts regarding “threshold setting” and “cutoff” are used to refine the optimization strategy. The assignment of data points to different cluster centers is done based on the newly developed weighted Euclidean distance instead of Euclidean distance. The developed approach is able to detect any type of cluster irrespective of their geometric shape. It is compared with four well-established clustering techniques. It is further applied for automatic segmentation of grayscale and color images, and its performance is compared with other existing techniques. For real-life datasets, statistical analysis is done. The technique shows its effectiveness and the usefulness.

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A new hybridization strategy for krill herd algorithm and harmony search algorithm applied to improve the data clustering

Proceedings of the First EAI International Conference on Computer Science and Engineering ◽

10.4108/eai.27-2-2017.152255 ◽

2017 ◽

Cited By ~ 9

Author(s):

Laith Mohammad Abualigah ◽

Ahamad Tajudin Khader ◽

Mohammed Azmi AlBetar ◽

Essam Said Hanandeh

Keyword(s):

Data Clustering ◽

Search Algorithm ◽

Harmony Search ◽

Harmony Search Algorithm ◽

Krill Herd Algorithm ◽

Krill Herd

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A novel harmony search algorithm and its application to data clustering

Applied Soft Computing ◽

10.1016/j.asoc.2020.106273 ◽

2020 ◽

Vol 92 ◽

pp. 106273 ◽

Cited By ~ 1

Author(s):

Kazem Talaei ◽

Amin Rahati ◽

Lhassane Idoumghar

Keyword(s):

Data Clustering ◽

Search Algorithm ◽

Harmony Search ◽

Harmony Search Algorithm

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A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications

Applied Sciences ◽

10.3390/app10113827 ◽

2020 ◽

Vol 10 (11) ◽

pp. 3827 ◽

Cited By ~ 11

Author(s):

Laith Abualigah ◽

Ali Diabat ◽

Zong Woo Geem

Keyword(s):

Data Clustering ◽

Search Algorithm ◽

Harmony Search ◽

Harmony Search Algorithm ◽

Wireless Sensor ◽

Future Research ◽

Research Directions ◽

Comprehensive Survey ◽

Future Research Directions ◽

Swarm Intelligence Optimization

The Harmony Search Algorithm (HSA) is a swarm intelligence optimization algorithm which has been successfully applied to a broad range of clustering applications, including data clustering, text clustering, fuzzy clustering, image processing, and wireless sensor networks. We provide a comprehensive survey of the literature on HSA and its variants, analyze its strengths and weaknesses, and suggest future research directions.

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