Fuzzy Clustering Algorithms with Applications to Rule Extraction

Gene Expression Data Analysis Using Feature Weighted Robust Fuzzy c-Means Clustering

Clustering of gene expression data has been proven to be very useful in various applications, i.e., identifying the natural structure inherent in gene expression, understanding gene functions, mining relevant information from noisy data, and understanding gene regulation. In all these applications, genes, i.e., features, play a crucial role in characterizing them into different groups. These features may be relevant, irrelevant, or redundant, but they have different contributions during the clustering process. This paper presents a novel approach by considering the effect of features during the clustering process. In the proposed method, the fuzzy c-means the objective function is modified using a weighted Euclidean distance between the features with a monotonically decreasing function. The monotonically decreasing function helps control the features' contribution during the clustering process to partition the data into more relevant clusters. The proposed approach is validated, and performance is presented in various clustering performance measures on the different standard datasets. These clustering performance measures have also been compared with multiple state-of-the-art methods.

Effects of mobile phone emissions on human red blood cells

Raman spectroscopy was performed on GSM 900 and 1800 MHz mobile phone signal exposed red blood cells (RBCs). The observed changes in the Raman spectra of mobile signal exposed RBCs compared to unexposed control suggest reduced hemoglobin-oxygen affinity for the exposed cells. The possible mechanism may involve activation of the voltage gated membrane Ca²⁺ channels by the mobile phone emissions resulting in an increase in the levels of adenosine triphosphate (ATP) and 2,3-diphosphoglycerate (2,3-DPG) in cells via altered metabolic activities. Further studies carried out with fluorescent Ca²⁺ indicator confirmed increased intracellular Ca²⁺ level in the exposed cells. Since intracellular ATP level influences the shape and mechanics of RBCs, exposed cells were studied using diffraction phase microscopy and optical tweezers. Detectable changes in shape and mechanical properties were observed due to mobile signal exposure.

Power Quality Analysis Using a Hybrid Model of the Fuzzy Min-Max Neural Network and Clustering Tree

A hybrid intelligent model comprising a modified fuzzy min-max (FMM) clustering neural network and a modified clustering tree (CT) is developed. A review of clustering models with rule extraction capabilities is presented. The hybrid FMM-CT model is explained. We first use several benchmark problems to illustrate the cluster evolution patterns from the proposed modifications in FMM. Then, we employ a case study with real data related to power quality monitoring to assess the usefulness of FMM-CT. The results are compared with those from other clustering models. More importantly, we extract explanatory rules from FMM-CT to justify its predictions. The empirical findings indicate the usefulness of the proposed model in tackling data clustering and power quality monitoring problems under different environments.

Least-Squares Support Vector Machine Approach to Viral Replication Origin Prediction

Replication of their DNA genomes is a central step in the reproduction of many viruses. Procedures to find replication origins, which are initiation sites of the DNA replication process, are therefore of great importance for controlling the growth and spread of such viruses. Existing computational methods for viral replication origin prediction have mostly been tested within the family of herpesviruses. This paper proposes a new approach by least-squares support vector machines (LS-SVMs) and tests its performance not only on the herpes family but also on a collection of caudoviruses coming from three viral families under the order of caudovirales. The LS-SVM approach provides sensitivities and positive predictive values superior or comparable to those given by the previous methods. When suitably combined with previous methods, the LS-SVM approach further improves the prediction accuracy for the herpesvirus replication origins. Furthermore, by recursive feature elimination, the LS-SVM has also helped find the most significant features of the data sets. The results suggest that the LS-SVMs will be a highly useful addition to the set of computational tools for viral replication origin prediction and illustrate the value of optimization-based computing techniques in biomedical applications.