Knowledge Discovery in Databases (KDD) is the process of extracting previously unknown, hidden and interesting patterns from a huge amount of data stored in databases. Data mining is a stage of the KDD process that aims at selecting and applying a particular data mining algorithm to extract an interesting and useful knowledge. It is highly expected that data mining methods will find interesting...

There is an extensive literature on data mining techniques, including several applications of these techniques in the e-commerce setting. However, all previous approaches require that expert users interpret the data mining results, making them cumbersome to use by business analysts. In this work, we describe a framework that shows how data mining technology can be effectively applied in an e-co...

One of the intended consequences of utilizing simulations in dynamic, data-driven application systems is that the simulations will adjust to new data as it arrives. These adjustments will be difficult because of the unpredictable nature of the world and because simulations are so carefully tuned to model specific operating conditions. Accommodating new data may require adapting or replacing num...

Temporal consistency is a strong cue in continuous data streams and especially in videos. We exploit this concept and encode temporal relations between consecutive frames using discriminative slow feature analysis. Activities are automatically segmented and represented in a hierarchical coarse to fine structure. Simultaneously, they are modeled in a generative manner, in order to analyze unseen...

A Dynamic Data Driven Application Systems (DDDAS) methodology is developed for evaluation of fluid-thermal systems wherein a complete specification of the boundary conditions is not known a priori and experimental diagnostics are restricted to a limited region of the flowfield. The Closed Loop formulation of DDDAS is used whereby experiment and simulation are synergized in an iterative manner t...

Traditionally, knowledge actionability has been investigated mainly by developing and improving technical interestingness. Recently, initial work on technical subjective interestingness and business-oriented profit mining presents general potential, while it is a long-term mission to bridge the gap between technical significance and business expectation. In this paper, we propose a two-way sign...

This article describes an application that illustrates the role of data mining technology in identifying hidden causal knoledge from health and medical data repositories. Across the health care and medical enterprises, a wide variety of data is being generated at a rapid rate. Current information technologies tends to focus on a more statical side of causal knowledge and do not address the dyna...

Modern Concepts & Developments in Agronomy Data-Driven Agronomy: Using Big Data to Revolutionize Farming Meshach Ojo Aderele* Department of Agriculture, USA *Corresponding author: Aderele, Beirut, Lebanon, Submission: February 21, 2023;Published: March 03, 2023 DOI: 10.31031/MCDA.2023.12.000790 ISSN 2637-7659Volume12 Issue 3