Knowledge Extraction from Data Using Graphs

ISBN-13: 9786204207636 / Angielski / Miękka / 116 str.

Abdeslam Jakimi; Zakariyaa Ait El Mouden; Moha Hajar

Opis

Graph analytics are now considered the state-of-the-art in many applications of communities detection. The combination between the graph's definition in mathematics and the graphs in computer science as an abstract data structure is the key behind the success of graph-based approaches in machine learning. Based on graphs, several approaches have been developed such as shortest path first (SPF) algorithms, subgraphs extraction, social media analytics, transportation networks, bioinformatic algorithms, . . . etc. While SPF algorithms are widely used in optimization problems, Spectral clustering (SC) algorithms have overcome the limits of the most state-of-art approaches in communities detection.The purpose of this study is to introduce a graph-based approach of communities detection data modelled by graphs. The motivation behind this work is to overcome the limitations of multiclass classification, as SC is an unsupervised clustering algorithm, there is no need to predefine the output clusters as a preprocessing step.

O autorze

Jakimi, AbdeslamZ. Ait El Mouden is an assistant professor of computer engineering at EST Meknes, UMI, Morocco ---A. Jakimi is a full professor of computer engineering in the department of computer science at FST Errachidia, UMI, Morocco ---Moha Hajar is a a full professor of mathematics in the department of mathematics at FST Errachidia, UMI, Morocco.