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Clustering is a technique of grouping data into a homogeneous structure according to the similarity or dissimilarity measures between objects. In clustering, the fuzzy c-means (FCM) algorithm is the best-known and most commonly used method and is a fuzzy extension of k-means in which FCM has been widely used in various fields. Although FCM is a good clustering algorithm, it only treats data points with feature components under equal importance and has drawbacks for handling high-dimensional data. The rapid development of social media and data acquisition techniques has led to advanced methods of collecting and processing larger, complex, and high-dimensional data. However, with high-dimensional data, the number of dimensions is typically immaterial or irrelevant. For features to be sparse, the Lasso penalty is capable of being applied to feature weights. A solution for FCM with sparsity is sparse FCM (S-FCM) clustering. In this paper, we propose a new S-FCM, called S-FCM-Lasso, which is a new type of S-FCM based on the Lasso penalty. The irrelevant features can be diminished towards exactly zero and assigned zero weights for unnecessary characteristics by the proposed S-FCM-Lasso. Based on various clustering performance measures, we compare S-FCM-Lasso with the S-FCM and other existing sparse clustering algorithms on several numerical and real-life datasets. Comparisons and experimental results demonstrate that, in terms of these performance measures, the proposed S-FCM-Lasso performs better than S-FCM and existing sparse clustering algorithms. This validates the efficiency and usefulness of the proposed S-FCM-Lasso algorithm for high-dimensional datasets with sparsity. [ABSTRACT FROM AUTHOR]

In this report, aqueous phase green synthesis of iron oxide nanoparticle utilizing Platanus orientalis is elucidated for the first time. The phytoconstituents of the P. orientalis leaf extract serve a dual role as reducing and capping agent during the fabrication of iron oxide nanoparticles. The role of the leaf extract in the synthesis of iron oxide has been briefly demonstrated in this work. The tailored iron oxide particles were characterized using X-ray diffraction, scanning electron microscopy, transmission electron microscopy, energy dispersive X-ray analysis, infrared spectroscopy, ultraviolet-visible spectroscopy, and dynamic light scattering technique. Nonetheless, X-ray diffraction pattern reveals the mixed phase nature of the ensuing iron oxide, i.e. α-Fe2O3 and γ-Fe2O3. The spherical oxide particles have an average diameter of 38 nm as determined from transmission electron microscopy. Infrared spectroscopy results confirmed the stabilization of iron oxide nanoparticles by the phytochemicals present in the leaf extract. Iron oxide nanoparticles show significant antifungal activity against Aspergillus niger and Mucor piriformis, employed as model fungi, but found to be more active toward M. piriformis.