Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm called Bilateral Weighted Fuzzy CMeans (BWFCM). We used a new objective function that uses some kinds of weights for reducing the effect of noises in clustering. Experimental results using, two artificial datasets, five real datasets, viz., Iris, Cancer, Wine, Glass and a speech corpus used in a GMM-based speaker identification task show that compared to three well-known clustering algorithms, namely, the Fuzzy Possibilistic C-Means, Credibilistic Fuzzy C-Means and Density Weighted Fuzzy C-Means, our approach is less sensitive to outliers and noises and has an acceptable computational complexity.

Wireless sensor networks (WSNs) consist of a large number of sensor nodes that allow users to accurately monitor a remote environment by aggregating the data from the individual nodes. These networks require robust and energy-efficient protocols that are improved reliability and lifetime of WSNs. clustering of sensor nodes is an emerging paradigm for the energy-efficient approach to improve lifetime and the reliability of WSN by reducing energy consumption. In this paper, a new Energy-efficient Weighted multi-level clustering Protocol (EWCP) is proposed. Cluster heads (CHs) are selected based on the allotted weight to each sensor nodes. The weight includes the parameters of sensors such as density, residual energy, and distance to prolong the network's lifetime and increase its efficiency. Also, the cluster members are selected based on their distance to the selected CHs. The lifetime of EWCP is improved significantly to compare with the other protocols. This improvement is attributed to the fact that EWCP is energy-efficient in clustering protocol.

Outliers are data values that lie away from the general cluster of other data values. Detecting the outliers of a dataset is an important research topic for data cleaning and finding new useful knowledge in many research areas, i.e. data mining, pattern recognition, etc. In the past decades, many useful algorithms were proposed in the literature. In this paper, a new fuzzy kernel-clustering algorithm with outliers (FKCO) is presented to locate critical areas that are often represented by only a few outliers. Theoretic analysis also shows that FKCO can converge to a local minimum of the objective function. Finally, based on the information theory, a new criterion for finding outliers is also proposed. Simulations of different types of datasets demonstrate the feasibility of this new method.

Unsupervised methods have become essential for managing and analyzing large datasets due to their ability to uncover underlying patterns without labeled data. In particular, unsupervised learning techniques have shown promise in enhancing the robustness of feature vectors, which can subsequently improve the accuracy and efficiency of classification models. This study presents a novel approach called Automatic Weighted clustering (AWC), designed as an unsupervised algorithm specifically aimed at optimizing data segmentation to support and enhance the classification performance of supervised algorithms. The AWC algorithm, applied in the context of medical datasets, enables efficient knowledge discovery by automatically identifying and weighting important data features. To assess AWC’s performance, we conducted extensive experiments across various datasets to test the algorithm’s generalizability and scalability. The AWC approach yielded nine distinct clusters from the dataset, demonstrating a 6.26% accuracy improvement compared to the standard feature vector and a 4.83% increase over the traditional K-means clustering method. Additionally, AWC achieved a 30.68% reduction in execution time, highlighting its potential for faster, more accurate medical data analysis and classification.

As a measure of relative variability, the coefficient of variation (CV) is a valuable charting statistic in statistical process control. Great efforts have been devoted to monitoring CV efficiently. To further improve the performance of CV charts, this paper proposes three Double Exponentially Weighted Moving Average (DEWMA) charts by incorporating Variable Sampling Interval (VSI) strategies to monitor the CV squared. The run length properties of the proposed charts are evaluated via Monte Carlo simulations. Comparative studies show that the proposed VSI DEWMA CV charts detect the process shifts faster than the existing CV charts. A real data example is presented to illustrate the VSI DEWMA CV charts.