K-means clustering algorithm is a partitional clustering algorithm that has been used widely in many applications for traditional clustering due to its simplicity and low computational complexity. This clustering technique depends on the user specification of the number of clusters generated from the dataset, which affects the clustering results. Moreover, random initialization of cluster centers results in its local minimal convergence. Automatic clustering is a recent approach to clustering where the specification of cluster number is not required. In automatic clustering, natural clusters existing in datasets are identified without any background information of the data objects. Nature-inspired metaheuristic optimization algorithms have been deployed in recent times to overcome the challenges of the traditional clustering algorithm in handling automatic data clustering. Some nature-inspired metaheuristics algorithms have been hybridized with the traditional K-means algorithm to boost its performance and capability to handle automatic data clustering problems. This study aims to identify, retrieve, summarize, and analyze recently proposed studies related to the improvements of the K-means clustering algorithm with nature-inspired optimization techniques. A quest approach for article selection was adopted, which led to the identification and selection of 147 related studies from different reputable academic avenues and databases. More so, the analysis revealed that although the K-means algorithm has been well researched in the literature, its superiority over several well-established state-of-the-art clustering algorithms in terms of speed, accessibility, simplicity of use, and applicability to solve clustering problems with unlabeled and nonlinearly separable datasets has been clearly observed in the study. The current study also evaluated and discussed some of the well-known weaknesses of the K-means clustering algorithm, for which the existing improvement methods were conceptualized. It is noteworthy to mention that the current systematic review and analysis of existing literature on K-means enhancement approaches presents possible perspectives in the clustering analysis research domain and serves as a comprehensive source of information regarding the K-means algorithm and its variants for the research community.

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

In railways, using a track- and ride-quality monitoring system on in-service train has become desirable for coordination and security. Identification of the track- or train-related rough rides via ...

Railway track location estimation using onboard inertial sensors

The data analysis process includes data grouping techniques based on data transformation, data cleaning, model building, and similar characteristics to understand important information from the data. The clustering method is an unsu-pervised learning model in machine learning. The principle of the clustering method is to divide, or group data points with similar characteristics into several small groups. The clustering method may provide optimal results in some instances so that not all cases can get appropriate results. It is what underlies the research in this paper. This paper analyzes data similarity according to the characteristics found and groups similar data objects into one cluster. The method used in this research uses the Chimpanzee Leader Election Optimization (CLEO) algorithm as an optimization algorithm in determining clustering and also a hybrid method with the K-means algorithm. The data analyzed to determine the performance of the model is iris data. Even though the data used is classification data, data object analysis is used to look for similarities between existing data. The elbow method functions to find the number of clusters that will be the input K to K-means. The model comparison in this paper uses three methods: data clustering based on the K-means method, the CLEO algorithm, and the combined Kmeans-CLEO. These results are evaluated and analyzed based on the input data and the method's performance. The results of the research that has been carried out show good modeling using the CLEO algorithm, while the hybrid algorithm is still below accuracy.

Kmeans - Chimpanzee Leader Election Optimization Algorithms-Based Data Analysis in Clustering Model

Data clustering plays a crucial role in the analysis of information collected from a variety of domains. Researchers developed many classical and mathematical algorithms to solve real-life problems, but due to the inherent property of these algorithms, they prematurely converge and fall to local optima. A further pattern of data in terms of shape, size, and distribution has a significant effect on the exploitation and exploration characteristic of algorithms which draw attention to many researchers. This work attempts to solve this problem by proposing an LNSMO local neighbour spider monkey optimization algorithm for data clustering. In the proposed algorithm Local Leader Phase of the spider monkey optimization algorithm is improved with its neighbour solution. Further to enhance the global search global leader phase of spider monkey optimization is improved with a chaotic operator. The performance of LNSMO is compared with eleven real-life datasets with five well-known Meta-heuristic algorithms in terms of a sum of within-cluster distance and convergence speed. It is further compared with recently developed hybrid meta-heuristic algorithms. Experimental result demonstrates that the proposed algorithm provides a better result in terms of Accuracy, F-measure, and SWCD.

Local neighbour spider monkey optimization algorithm for data clustering

Received Jan 16, 2023 Revised Mar 19, 2023 Accepted Apr 17, 2023 In sensor networks the main problem facing by many researchers is regarding the energy efficiency. Different protocols are evaluated for communicating between the sensor nodes. The routing protocol when combined with evolutionary algorithms gives best optimal solution for the problem incurred in wireless sensor networks. In this paper, ant lion energy efficient-power efficient gathering in sensor information systems (ALEEPEGASIS) is used to develop the chain. This technique can achieve a global optimization solution by finding the best cluster head or the leader node for data communication. The techniques help in distributing the paths equally while the transmission of data process is performed. By performing this process, the power consumption near the sensor nodes can be reduced. The proposed technique is compared with other techniques like energy efficient PEGASIS and swarm energy efficient PEGASIS. The parameters used to compare are number of alive nodes, number of dead nodes and residual energy. The performance is observed using MATLAB simulation results.

Design of ant lion optimization-based PEGASIS routing protocol for energy efficiency in networks

High-quality clustering algorithms play an essential role in the data analysis. Traditional clustering algorithms are susceptible to initial cluster centers, which leads to the degradation of clustering quality. Clustering analysis based on Swarm Intelligence (SI) optimization algorithms can solve the problem of traditional clustering algorithms by the better ability to find the optimal solutions, such as Glowworm Swarm Optimization (GSO) algorithm. The GSO-based clustering analysis can use the multi-modal optimization ability to search for the optimal cluster centers. This paper proposes two clustering techniques based on the GSO algorithm: one is to realize self-organizing clustering of data through improved GSO algorithm, and the other is to hybrid the improved GSO self-organizing clustering algorithm with the k-means algorithm. The proposed algorithms test on the Iris data set. The experimental results show that the enhanced GSO algorithm proposed in this paper can efficiently realize self-organization clustering of data without initializing cluster centers and cluster number, and the proposed hybrid algorithm has better clustering results over four clustering algorithms.

Using Improved Glowworm Swarm Optimization Algorithm for Clustering Analysis

M274 is a newly emerging rail transit project. The reliability and continuity of the maglev positioning system are directly related to the safe operation of the train. Maglev positioning system in the past program is simply a combination of sensors, to ensure that in the event of a sensor failure, you can redundant use of other sensor positioning. In this paper, we design a multi-information fusion maglev positioning system scheme, and experimentally proved its positioning accuracy is higher than the previous simple combination of the sensor program.

Research on Maglev Train Positioning System Based on Multi-Information Fusion

This paper reviews glowworm swarm optimization algorithm (GSO), which is a meta-heuristic swarm intelligence algorithm. The GSO algorithm is applied for solving optimization problems. Shortcoming of the GSO algorithm has been identified with the introduction and discussion of the improvement taken place in recent years. Adaptive step size and new movement rules have been widely used in the improvement of GSO algorithm. The application of GSO including clustering techniques is also presented. Very promising GSO clustering versions use MapReduce framework to improve the computational efficiency when the clustered data set is large and thereby reducing the time complexity.

/pdf/a-review-of-glowworm-swarm-optimization-algorithm-4mp5ypdb6t.pdf

A review of glowworm swarm optimization algorithm

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Using Improved Glowworm Swarm Optimization Algorithm for Clustering Analysis

Research on Maglev Train Positioning System Based on Multi-Information Fusion

A review of glowworm swarm optimization algorithm