For time series gene expression data, it is an important problem to find subgroups of genes with similar expression pattern in a consecutive time window. In this paper, we extend a fuzzy c-means clustering algorithm to construct two models to detect biclusters respectively, i.e., constant value biclusters and similarity-based biclusters whose gene expression profiles are similar within consecut...

Problem statement: The work presented Fuzzy Modeled K-means Cluster Quality Mining of hidden knowledge for Decision Support. Based on the number of clusters, number of objects in each cluster and its cohesiveness, precision and recall values, the cluster quality metrics is measured. The fuzzy k-means is adapted approach by using heuristic method which iterates the cluster to form an efficient v...

In the field of image processing and computer vision (CV), machine learning (ML) architectures are widely used. Image compression problems can be solved using convolutional neural networks (CNNs). As a result bandwidth memory constraints, images is necessity. There three types information found in images: useful, redundant, irrelevant. this survey, we will discuss how ML used to compress lossy ...

let h be a subgroup of a group g. h is said to be s-embedded in g if g has a normal t such that ht is an s-permutable subgroup of g and h ∩ t ≤ h sg, where h denotes the subgroup generated by all those subgroups of h which are s-permutable in g. in this paper, we investigate the influence of minimal s-embedded subgroups on the structure of finite groups. we determine the structure the finite grou...

Soria et al. have successfully identified six clinically useful and novel subgroups in the Nottingham Tenovus Breast Cancer dataset. However, the methodology used is semi-manual and no single clustering can automatically classify the dataset so far. In this work, two variations of semisupervised Fuzzy c-means (ssFCM) algorithms are explored to classify the Nottingham Tenovus Breast Cancer datas...