Anonymizing Graphs: Measuring Quality for Clustering

Figure 1 shows the intensity map of ion m/z 850.658, the clustering results with four clusters (fuzzifier set to 2 and using the Euclidean distance), and the hard and soft

The results for individual task graphs have been presented in Table III. Task graphs’ characteristics including task count, makespan, boundary value, and MTTF obtained by

Por lo tanto, gracias al uso de metodolog´ıas ´ agiles, al uso de principios y patrones de dise˜ no de software y a una serie de t´ ecnicas de desarrollo guiado por pruebas el

We recently introduced the negentropy increment, a validity index for crisp clustering that quantifies the average normality of the clustering partitions using the negentropy.

The values in the table 2 are average values of the surface roughness parameters and their standard deviation measured for hornbeam wood in three different altitude classes..

These networks based on metric learning techniques improve clustering and classification results over traditional categorical cross-entropy loss networks for plant

We have presented a method to protect categorical microdata based on a pre-clustering approach which performs better protections based on sta- tistical and clustering-based

To create genetic graph-based clustering algorithms and heuristics to improve the robustness and solutions of classical partitional clustering algorithms, and to study its