Application of agglomerative and partitional algorithms for the study of the phenomenon of the collaborative economy within the tourism industry

This research discusses the application of two different clustering algorithms (agglomerative and partitional) to a set of data derived from the phenomenon of the collaborative economy in the tourism industry known as Airbnb. In order to analyze this phenomenon, the algorithms are known as “hierarchical Tree” and “K-Means” were used with the objective of gaining a better understanding of the spatial configuration and current functioning of this complimentary lodging offer. The city of Guanajuato, Mexico was selected as the case for convenience purposes and the main touristic attractions were used as parameters to conduct the analysis. Cluster techniques were applied to both algorithms and the results were statistically compared

Additional file 1: Table S1. of Maximum basal FSH predicts reproductive outcome better than cycle-specific basal FSH levels: waiting for a “better" month conveys limited retrieval benefits

Clinical parameters for patients with and without a history of FSH elevation. Cycle data were categorized by presence or absence of elevated current FSH, Max FSH or PMax FSH (>13 mIU/mL) at the time of the cycle and stratified by age group. Incomplete (cancelled) cycles were excluded. Number of cycles with data for each parameter were noted. P-value comparisons were calculated between the elevated and non-elevated groups by t-test. Significance was noted as *** (p<0.001), ** (p<0.01) or * (p<0.05). (XLSX 42 kb