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An efficient meta-heuristic algorithm based on water flow optimizer for data clustering

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Abstract

Clustering is a popular data analysis technique that can explore the structure of data through cluster analysis. Similar data are put into the same cluster, while dissimilar data allocate to other clusters. The similarity/dissimilarity among data objects is determined using a distance function. Further, clustering algorithms aim to choose the optimal set of centroids for obtaining better partitioning, but clustering accuracy is always susceptible. This issue of clustering is addressed through meta-heuristic algorithms. This research also aims to handle the accuracy issue and presents a new algorithm for effective cluster analysis. The proposed clustering algorithm is inspired by a water flow optimizer (WFO). The WFO algorithm performance is validated on the well-defined clustering problems based on SSE, accuracy (AR) and detection rate (DR) parameters. The results indicate that the WFO algorithm gets higher clustering results in terms of SSE, AR and DR than the same class of algorithms. The performance is also validated using Friedman statistical test followed by a post hoc test. Results indicated that the proposed WFO gets better statistical results than other clustering algorithms.

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Abbreviations

AAA:

Artificial algae algorithm

ABC:

Artificial bee colony

ACO:

Ant colony optimization

AR:

Accuracy rate

BB-BC:

Big bang–big crunch

BH:

Black hole

CS:

Cuckoo search

CSS:

Charge system search

DR:

Detection rate

ECA:

Evolutionary center algorithm

EFO:

Electromagnetic field optimization

FA:

Firefly algorithm

FA-SOM:

Firefly algorithm-self organizing maps

FCM:

Fuzzy C-means

FKM:

Fuzzy k-modes

FPCOM:

Fuzzy C-means and Fuzzy C-ordered means

GA:

Genetic algorithm

GA-PFKM:

Genetic algorithm-possibilistic Fuzzy k-modes

GWO:

Grey wolf optimization

IGWO:

Improved grey wolf optimization

KM:

K-means

MOA:

Magnetic optimization algorithm

MoCS:

Modified cuckoo search

PSO:

Particle swarm optimization

PSO-FCM:

Particle swarm optimization-Fuzzy C-means

PSO-PFKM:

Particle swarm optimization-possibilistic Fuzzy k-modes

ROA:

Remora optimization algorithm

RSA:

Reptile search algorithm

SCA:

Sine cosine algorithm

SCA-PFKM:

Sine cosine algorithm-possibilistic Fuzzy k-modes

SMSHO:

Selfish herd optimization algorithm and simplex method

SOM:

Self-organizing maps

SSE:

Sum of squared error

UCI:

University of California Irvine

VNS:

Variable neighbourhood strategy

WFO:

Water flow optimizer

WWO:

Water wave optimization

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Author information

Authors and Affiliations

  1. Department of CSE, MRIIRS, Faridabad, India

    Ramesh Chandra Sahoo, Tapas Kumar & Poonam Tanwar

  2. Department of CST, MRU, Faridabad, India

    Jyoti Pruthi & Sanjay Singh

Authors
  1. Ramesh Chandra Sahoo
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  2. Tapas Kumar
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  3. Poonam Tanwar
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  4. Jyoti Pruthi
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  5. Sanjay Singh
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Contributions

RCS, TK design the research. RCS, TK, PT analyze the algorithm and implement it. RCS, JP, SS handle the data collection part and pre-processing part. RCS, TK, JP and SS did the result analysis part. RCS, TK, PT wrote the article and managed it.

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Correspondence to Ramesh Chandra Sahoo.

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Sahoo, R.C., Kumar, T., Tanwar, P. et al. An efficient meta-heuristic algorithm based on water flow optimizer for data clustering. J Supercomput 80, 10301–10326 (2024). https://doi.org/10.1007/s11227-023-05822-y

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