HCuRMD: Hierarchical Clustering Using Relative Minimal Distances

Initially, we applied the H-FCM algorithm (with m=1.1) to the test document collections for a number of clusters that matched the number of reference classes c REF and calculated

We apply a clustering approach based on that algorithm to the gene expression table from the dataset “The Cancer Cell Line Encyclopedia”.. The resulting partition well agrees with

To conclude, using the top 300 most frequent terms of each document and character 2-grams, a BCubed F-score of 0.54 is achieved on the, provided, training data with a threshold of

Complete author clustering: We do a detailed analysis, where we need to identify the number k of different authors (clusters) in a collection and assign each docu- ment to exactly

The experimental setup allows to compare the performances of GBC, K-Means and Ward’s hierarchical clustering 11 times, because we used the 8 data sets described in the section IV-C,

Finally, we have evaluated DAHCA on a real-world network like the Zachary’s karate club network, initially presented in [5] and known to be a vastly used benchmark for

Bartlett’s test for the equality of variances is used since long for determining the number of factors to retain in the context of a Principal Component Analysis (PCA) [3]. We show

Traditional HK clustering algo- rithm first determines to the initial cluster centers and the number of clusters by agglomerative algorithm, but agglomerative algorithm merges two