Extension of graph clustering algorithms based on SCAN method in order to target weighted graphs

In this thesis we evaluate current neighbour-based graph clustering algorithms: SCAN, DHSCAN, and AHSCAN. These algorithms possess the ability to identify special nodes in graphs such as hubs and outliers. We propose and extension for each of these in order to support weighted edges. We further implemented two graph generating frameworks to create test cases. In addition we used a graph derived from the ENRON email log. We also implemented a Fast Modularity clustering algorithm, which is considered as one of the top graph clustering algorithms nowadays. One of three sets of experiments showed that results produced by extended algorithms were better than one of the reference algorithms, in other words more nodes were classified correctly. Other experiments revealed some limitations of the newly proposed methods where we noted that they do not perform as well on other types of graphs. Hence, the proposed algorithms perform best on social graphs with pronounced community structure

Weighted SCAN for Modeling Cooperative Group Role Dynamics

Abstract—Social agents have the ability of communicating and forming groups with each other. Group members in games typically share the same role. In dynamic environments with the presence of obstacles and barriers separating members from each other presents a situation where a member separated from the rest of the group, while still a member of that group, should not have the same role or updates of the rest of the group due to the physical distance presented by the obstacles. This study introduces a weighted version of the SCAN and hierarchical SCAN graph clustering algorithms which are essentially based on neighbors. The autonomous agent players in spatial strategy game scenarios tested with the weighted SCAN have demonstrated an improved realistic behaviour in the social test settings. I

Memetic Algorithms for Business Analytics and Data Science: A Brief Survey

This chapter reviews applications of Memetic Algorithms in the areas of business analytics and data science. This approach originates from the need to address optimization problems that involve combinatorial search processes. Some of these problems were from the area of operations research, management science, artificial intelligence and machine learning. The methodology has developed considerably since its beginnings and now is being applied to a large number of problem domains. This work gives a historical timeline of events to explain the current developments and, as a survey, gives emphasis to the large number of applications in business and consumer analytics that were published between January 2014 and May 2018