Clustering applications dealing with perception based or biased data lead to models with non-disjunct clusters. There, objects to be clustered are allowed to belong to several clusters at the same time which results in a fuzzy clustering. It can be shown that this is equivalent to searching all maximal cliques in dynamic graphs like G t = (V, E t ), where E t−1 ⊂ E t , t = 1, . . . , T ; E 0 = φ. In this article algorithms are provided to track all maximal cliques in a fully dynamic graph. It is naturally to raise the question about the maximum clique, having all maximal cliques. Therefore this article discusses potentials and drawbacks for this problem as well.
Motivated by IT evaluation problems identified in a large public sector organization, we propose how evaluation requirements can be supported by a framework combining different models and methods from IS evaluation theory. The article extends the content, context, process (CCP) perspectives of organizational change with operations research techniques and demonstrates the approach in practice for an Enterprise Resource Planning evaluation.
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