In the rapidly evolving field of robotics, integration of commonsense knowledge (CSK) in AI
systems is becoming highly crucial to enhance the decision-making capabilities of robots …

The aim of this paper is to extract knowledge using predictive apriori and distributed grid
based apriori algorithms for association rule mining. The paper presents the implementation …

Renewable energy sources are vital to help mitigate the effects of climate change, and
reducing the carbon dioxide emissions of fossil fuels, eg the state of New Jersey has a goal …

The analytical process designed to mine data became more difficult with the rapid
information explosion. This in-turn created completely distributed and un-indexed data. Thus …

This work mines big data in Sentinel-1 satellite images to unveil geographical patterns in
offshore wind energy. We leverage unsupervised machine learning to extract insights from a …

Ever growing size of information and database has always demanded the scientific world for
very efficient rule mining algorithm. This paper gives an extension to the Apriori algorithm, a …

In order to develop a data mining system to extract the fuzzy inference rules from the data, in
this paper a fuzzy inference algorithm based on quantitative association rule (FI-QAR) is …

Big data analytics examines large amounts of data to uncover hidden patterns, correlations
and other insights. In this work, a novel association rule-mining algorithm is employed for …

Distributed data mining (DDM) techniques have become necessary for large and multi-
scenario datasets requiring resources, which are heterogeneous and distributed in nature …

Official data, administered by National Statistic Institutes (NSIs), play crucial role for being a
major element of the governmental economic and social decision‐making process. This …