A Self-Supervised Approach for Cluster Assessment of High-Dimensional Data

Estimating the number of clusters and underlying cluster structure in a dataset is a crucial task. Real-world data are often unlabeled, complex and high-dimensional, which makes it difficult for traditional clustering algorithms to perform well. In recent years, a matrix reordering based algorithm, called "visual assessment of tendency" (VAT), and its variants have attracted many researchers from various domains to estimate the number of clusters and inherent cluster structure present in the data. However, these algorithms fail when applied to high-dimensional data due to the curse of dimensionality, as they rely heavily on the notions of closeness and farness between data points. To address this issue, we propose a deep-learning based framework for cluster structure assessment in complex, image datasets. First, our framework generates representative embeddings for complex data using a self-supervised deep neural network, and then, these low-dimensional embeddings are fed to VAT/iVAT algorithms to estimate the underlying cluster structure. In this process, we ensured not to use any prior knowledge for the number of clusters (i.e k). We present our results on four real-life image datasets, and our findings indicate that our framework outperforms state-of-the-art VAT/iVAT algorithms in terms of clustering accuracy and normalized mutual information (NMI).Comment: Submitted to IEEE SMC 202

An Enhanced Sampling-Based Viewpoints Cosine Visual Model for an Efficient Big Data Clustering

Bunching is registering the item's similitude includes that can be utilized to segment the information. Object similarity (or dissimilarity) features are taken into account when locating relevant data object clusters. Removing the quantity of bunch data for any information is known as the grouping inclination. Top enormous information bunching calculations, similar to single pass k-implies (spkm), k-implies ++, smaller than usual group k-implies (mbkm), are created in the groups with k worth. By and by, the k worth is alloted by one or the other client or with any outside impedance. Along these lines, it is feasible to get this worth immovable once in a while. In the wake of concentrating on related work, it is researched that visual appraisal of (bunch) propensity (Tank) and its high level visual models extraordinarily decide the obscure group propensity esteem k. Multi-perspectives based cosine measure Tank (MVCM-Tank) utilized the multi-perspectives to evaluate grouping inclination better. Be that as it may, the MVCM-Tank experiences versatility issues in regards to computational time and memory designation. This paper improves the MVCM-Tank with the inspecting methodology to defeat the versatility issue for large information grouping. Trial investigation is performed utilizing the enormous gaussian engineered datasets and large constant datasets to show the effectiveness of the proposed work

Clustering and Visualization of Fuzzy Communities In Social Networks

Abstract-We discuss a new formulation of a fuzzy validity index that generalizes the Newman-Girvan (NG) modularity function. The NG function serves as a cluster validity functional in community detection studies. The input data is an undirected graph G = (V, E) that represents a social network. Clusters in V correspond to socially similar substructures in the network. We compare our fuzzy modularity to an existing modularity function using the well-studied Karate Club data set

System Architecture Design Using Multi-Criteria Optimization

System architecture is defined as the description of a complex system in terms of its functional requirements, physical elements and their interrelationships. Designing a complex system architecture can be a difficult task involving multi-faceted trade-off decisions. The system architecture designs often have many project-specific goals involving mix of quantitative and qualitative criteria and a large design trade space. Several tools and methods have been developed to support the system architecture design process in the last few decades. However, many conventional problem solving techniques face difficulties in dealing with complex system design problems having many goals. In this research work, an interactive multi-criteria design optimization framework is proposed for solving many-objective system architecture design problems and generating a well distributed set of Pareto optimal solutions for these problems. System architecture design using multi-criteria optimization is demonstrated using a real-world application of an aero engine health management (EHM) system. A design process is presented for the optimal deployment of the EHM system functional operations over physical architecture subsystems. The EHM system architecture design problem is formulated as a multi-criteria optimization problem. The proposed methodology successfully generates a well distributed family of Pareto optimal architecture solutions for the EHM system, which provides valuable insights into the design trade-offs. Uncertainty analysis is implemented using an efficient polynomial chaos approach and robust architecture solutions are obtained for the EHM system architecture design. Performance assessment through evaluation of benchmark test metrics demonstrates the superior performance of the proposed methodology