clustering

Metaheuristics have proved highly effective in addressing optimization challenges. Various algorithms address the clustering problem to find optimal centers for the clusters. One of the disadvantages of some of these algorithms is stagnation in local optima, especially for big data. If this problem is not properly solved, the clustering process will suffer. This research introduces a new hybrid method by merging the capabilities of two metaheuristic algorithms: Harris hawks optimization algorithm (HHO) and slime mould algorithm (SMA). These metaheuristic methods are employed to determine the best location for the cluster centers. Optimization aims to reduce intra-cluster distance. In other words, the data points of each cluster should be close to its cluster center and also to avoid local optima. The effectiveness of these techniques is assessed and contrasted with the SMA and HHO algorithms on Iris, Vowel and Wine data sets. Compared to mentioned algorithms, our proposed method exhibits significantly improved convergence speed. The results also proved this method can properly find the optimal centers for clustering which finally improves the performance of the proposed method.

This paper is the second in the series celebrating the mathematical works of Professor Themba Dube. In this sequel, we give prominence to Dube's pivotal contributions on pointfree convergence at the unstructured frame level, in the category of locales, and on his noteworthy conceptions on extensions and frame quotients. We distill and draw attention to particular studies of Dube on filters and his novel characterizations of certain conservative pointfree properties by filter and ultrafilter convergence, notably normality, almost realcompactness, and pseudocompactness. We also feature Dube's joint work on convergence and clustering of filters in Loc and coconvergence and coclustering of ideals in the category Frm.

Wireless Sensor Networks (WSNs) encounter considerable challenges in terms of energy efficiency and network longevity due to their limited energy resources. This paper proposes a novel hybrid clustering-based routing protocol that addresses these challenges by integrating fuzzy logic for dynamic and adaptive cluster head (CH) selection based on residual energy, node degree, and proximity, and genetic algorithms (GA) for optimising cluster formation by balancing energy consumption and minimising communication distances. The protocol's objectives are threefold: to minimise energy consumption, extend network lifespan, and enhance Quality of Service (QoS).The proposed method was simulated in MATLAB and benchmarked against the LEACH and TEEN protocols. The results demonstrated the protocol's superior performance, achieving a 30% reduction in energy consumption, a 25% increase in network longevity, and higher data reliability. The primary factors contributing to this enhanced performance are the integrated use of fuzzy logic for optimised cluster head selection and genetic algorithms for optimal cluster formation. The findings substantiate the protocol's capacity to substantially enhance the energy efficiency and scalability of WSNs, providing a resilient and pragmatic solution for practical applications in real-world settings.

Cluster analysis in data envelopment analysis (DEA) is determining clusters for the units under evaluation regarding to their similarity. which measure of distances define their similarities. Over the years, researches have been carried out in the field of clustering of DMUs. In this paper, an algorithm for clustering units using projecting them on the frontier is presented. In fact, we gained for every decision making unit (DMU), nearest most productive scale size (MPSS) as target, to find number of clusters 2 method applied. Silhouette index was used to measure similarity value for our clustering. Numerical examples are provided to illustrate the proposed method and its results.

In the realm of agriculture and natural resources, medicinal plants stand out as a valuable resource. In recent years, faced with challenges such as predicting climate changes, soil classification, land use, and identifying patterns, there is a growing need for optimal techniques with higher efficiency, particularly in the cultivation of medicinal plants. Therefore, this article introduces the application of data mining to analyze available data in the agriculture and natural resources areas, focusing specifically on the medicinal plant industry. The primary objective is to explore data mining techniques that can enhance various aspects of medicinal plant cultivation, addressing challenges related to climate predictions, soil classification, and optimizing production. The article concludes by presenting the most effective data analysis methods in this domain, accompanied by their corresponding algorithms. Additionally, the aforementioned research is a guide for those intending to investigate the applications of data mining methods are highlighted for increased productivity, encompassing areas such as predicting crop yield, forecasting weather conditions, rainfall patterns, seed and plant conditions, soil quality, and medicinal plant production. The summarization and analysis of the outcome indicated that implementing AI could improve the design and process engineering strategies in bioprocessing fields.