A Review on K-means Clustering Based on Quantum Particle Swarm Optimisation Algorithm

Shilpa Meshram; Jayshree Boaddh

Shilpa Meshram Mittal Institute of Technology, Bhopal, India
Jayshree Boaddh Mittal Institute of Technology, Bhopal, India

Keywords: Data Mining, Unsupervised Learning, Clustering, QPSO-K-means Clustering Algorithm

Abstract

Unsupervised learning clustering techniques play a vital role in data mining, with a wide range of applications in unsupervised classification. Clustering is a method used to categorise data into meaningful groups. The k-means algorithm is a well-known clustering algorithm that aims to minimise the squared distance between feature values of points within the same cluster. In many applications, using an evolutionary computation technique called Quantum Particle Swarm Optimization (QPSO) in conjunction with the k-means algorithm has proven effective in finding suboptimal solutions. In this algorithm, the cluster centres are simulated as particles, allowing for the identification of suitable and stable cluster centres. This paper discusses the current improvement in the QPSO-k-means clustering algorithm, focusing on swarm initialisation and algorithm parameter optimisation. We validate the algorithm using the UCI healthcare dataset and demonstrate its ability to address suboptimal clustering by optimising parameters such as the number of iterations, error rate, and optimal solution for cluster centres. The minimisation factor of the validation parameter indicates the compactness and validity of the clustering algorithm.

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References

. Jayamalini, K., and M. Ponnavaikko. “Research on web data mining concepts, techniques and applications.” In 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET), pp. 1-5. IEEE, 2017.

. Gera, Mansi, and Shivani Goel. “Data mining-techniques, methods and algorithms: A review on tools and their validity.” International Journal of Computer Applications 113, no. 18, 2015.

. Sisodia, Deepti, Lokesh Singh, Sheetal Sisodia, and Khushboo Saxena. “Clustering techniques: a brief survey of different clustering algorithms.” International Journal of Latest Trends in Engineering and Technology (IJLTET) 1, no. 3: 82-87, 2012.

. Zou, Hailei. “Clustering algorithm and its application in data mining.” Wireless Personal Communications 110, no. 1: 21-30, 2020.

. Mythili, S., and E. Madhiya. “An analysis on clustering algorithms in data mining.” International Journal of Computer Science and Mobile Computing 3, no. 1 334-340, 2014.

. J. Liu, L. Han and L. Hou, “K-Mean Clustering Algorithm Based on Particle Swarm Optimisation,” System Engineering Theory and Practice, vol. 06, pp. 54-58, 2005.

. Bai, Lili, Zerui Song, Haijie Bao, and Jingqing Jiang. “K-Means clustering based on improved quantum particle swarm optimisation algorithm.” In 2021 13th International Conference on Advanced Computational Intelligence (ICACI), pp. 140-145. IEEE, 2021.

. Salem, Semeh Ben, Sami Naouali, and ZiedChtourou. “A fast and effective partitional clustering algorithm for large categorical datasets using a k-means based approach.” Computers & Electrical Engineering 68, 463-483, 2018.

. Patibandla, RSM Lakshmi, and N. Veeranjaneyulu. “Survey on clustering algorithms for unstructured data.” In Intelligent Engineering Informatics, pp. 421-429. Springer, Singapore, 2018.

. Rezaee Jordehi, Ahmad, and Jasronita Jasni. “Particle swarm optimisation for discrete optimisation problems: a review.” Artificial Intelligence Review 43: 243-25, 2015.

. Poli, Riccardo. “Analysis of the publications on the applications of particle swarm optimisation.” Journal of Artificial Evolution and Applications 2008: 1-10, 2008.

. Arai, Kohei, and Ali Ridho. “Hierarchical K-means: an algorithm for centroids initialisation for K-means.”, Reports of the Faculty of Science and Engineering, Vol. 36, No.1, 2007.

. R. Chouhan and A. Purohit, “An approach for document clustering using PSO and K-means algorithm,” in 2018 2nd International Conference on Inventive Systems and Control (ICISC), IEEE, pp. 1380-1384, 2018.

. R. Janani and S. Vijayarani, “Text document clustering using spectral clustering algorithm with particle swarm optimisation,” Expert Systems with Applications, vol. 134, pp. 192-200, 2019.

. Barakbah, Ali Ridho, and Kohei Arai. “Centronit: Initial Centroid Designation Algorithm for K-Means Clustering.” EMITTER International journal of engineering technology 2, no. 1: 50-62, 2014.

. Caron, Mathilde, Piotr Bojanowski, Armand Joulin, and Matthijs Douze. “Deep clustering for unsupervised learning of visual features.” In Proceedings of the European Conference on Computer Vision (ECCV), pp. 132-149. 2018.