Real-world data sets are regularly provides different and complementary features of information in an unsupervised way. Different types of algorithms have been proposed recently in the genre of cluster analysis. It is arduous to the user to determine well in advance which algorithm would be the most suitable for a given dataset. Techniques with respect to graphs are provides excellent results for this task. However, the existing techniques are easily vulnerable to outliers and noises with limited idea about edges comprised in the tree to divide a dataset. Thus, in some fields, the necessacity for better clustering algorithms it uses robust and dynamic methods to improve and simplify the entire process of data clustering has become an important research field. In this paper, a novel distance-based clustering algorithm called the entropic distance based K-means clustering algorithm (EDBK) is proposed to eradicate the outliers in effective way. This algorithm depends on the entropic distance between attributes of data points and some basic mathematical statistics operations. In this work, experiments are carry out using UCI datasets showed that EDBK method which outperforms the existing methods such as Artificial Bee Colony (ABC), k-means.

In various sectors such as defense and mining, existing Unmanned Ground Vehicle (UGV) applications face specific limitations, including the inability to achieve wide- range control and operate in dark environments. To mitigate human risks and reduce human involvement in bomb disposal operations, the need for UGVs has become crucial. In this context, we propose a model/framework that utilizes gas and temperature sensors to detect environmental conditions. Our framework aims to enhance UGV applications by incorporating wide-range Wi-Fi enabled control devices and night mode camera functionality. This enables rapid object detection over a greater distance and emphasizes remote control through a mobile application. As a result, the proposed system automates the process of bomb disposal, thereby reducing human risks involved in such operations.