Algorithms for clustering data
Algorithms for clustering data
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Boosting the detection of modular community structure with genetic algorithms and local search
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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The roll calls of the Italian Parliament in the current legislature is studied by employing multidimensional scaling, hierarchical clustering, and network analysis. In order to detect changes in voting behavior, the roll calls have been divided in seven periods of six months each. All the methods employed pointed out an increasing fragmentation of the political parties endorsing the previous government that culminated in its downfall. By using the concept of modularity at different resolution levels, we identify the community structure of Parliament and its evolution in each of the time periods considered. The analysis performed revealed as a valuable tool in detecting trends and drifts of Parliamentarians. It showed its effectiveness at identifying political parties and at providing insights on the temporal evolution of groups and their cohesiveness, without having at disposal any knowledge about political membership of Representatives.