Recent Advances in Modularity Optimization and Their Application in Retailing

In this contribution we report on three recent advances in modularity optimization, namely: 1. The randomized greedy (RG) family of modularity optimization algorithms are state-of-the-art graph clustering algorithms which are near optimal, fast, and scalable. 2. The extension of the RG family to multi-level clustering. 3. A new entropy based cluster index which allows the detection of the proper clustering levels and of stable core clusters at each level. Last, but not least, several marketing applications of these algorithms for customer enablement and empowerment are discussed: e.g. the detection of low-level cluster structures from retail purchase data, the analysis of the co-usage structure of scientific documents for detecting multilevel category structures for scientific libraries, and the analysis of social groups from the friend relation of social network sites

An ensemble learning strategy for graph clustering

Abstract. This paper is on a graph clustering scheme inspired by ensemble learning. In short, the idea of ensemble learning is to learn several weak classifiers and use these weak classifiers to determine a strong classifier. In this contribution, we use the generic procedure of ensemble learning and determine several weak graph clusterings (with respect to the objective function). From the partition given by the maximal overlap of these clusterings (the cluster cores), we continue the search for a strong clustering. We demonstrate the performance of this scheme by using it to maximize the modularity of a graph clustering. We show, that the quality of the initial weak clusterings is of minor importance for the quality of the final result of the scheme if we iterate the process of restarting from maximal overlaps

Cognitive Function Is Impaired in Patients with Recently Diagnosed Type 2 Diabetes, but Not Type 1 Diabetes

Objective. To test whether cognitive function is impaired in early states of diabetes and to identify possible risk factors for cognitive impairment. Methods. A cross-sectional analysis within the German Diabetes Study included patients with type 1 or type 2 diabetes within the first year after diagnosis or five years after study inclusion and metabolically healthy individuals. Participants underwent comprehensive metabolic phenotyping and testing of different domains of cognitive function. Linear regression models were used to compare cognition test outcomes and to test associations between cognitive function and possible influencing factors within the groups. Results. In participants with recently diagnosed diabetes, verbal memory was poorer in patients with type 2 diabetes (P=0.029), but not in type 1 diabetes (P=0.156), when compared to healthy individuals. Five years after diagnosis, type 2 diabetes patients also showed lower verbal memory than those with type 1 diabetes (P=0.012). In addition to crystallized intelligence, a higher body mass index among individuals with recently diagnosed type 2 diabetes and male sex among individuals with recently diagnosed type 1 diabetes were associated with impaired verbal memory (all P<0.05). Conclusion. Verbal memory is impaired in individuals with recently diagnosed type 2 diabetes and likely associated with higher body mass. This trial is registered with the trial registration number NCT01055093