According to both robustness measures, clustering (text {C}_3) performs better throughout the perturbation scenarios.
In the current matrix (mathbf{W}_I), clustering (text {C}_2) obviously takes value one, while the other cluster assignments take value zero.
However, (text {C}_3) is closely followed by clustering (text {C}_2) according to measure (widehat{R_2^{mathcal {X}}}), even if in terms of measure (widehat{R_1^{mathcal {X}}}) clustering (text {C}_2) appears to be the best for only 24.7% of the randomly perturbed adjacency matrices.
The taxonomic organization involved breaking down the transcripts into fragments of text, clustering text around single words or phrases, coding the clusters of text, organizing those clusters by concepts and then identifying thematic content from these concepts.
Clustering takes the essential comparability of text features primarily to cluster text features into consideration.
The number of messages transferring in one cluster (text {Packet}_{text {in}_{text {one}}}phantom {dot {i}!}) and the number of messages transferring across clusters (text {Packet}_{text {in}_{text {across}}}phantom {dot {i}!}) calculate by super controller based on polling least-connection algorithm.
Under the circumstances that the storage space is insufficient or the storage space of the cluster text system is too massive, the HBase database cluster dynamic updating and optimizing the database space are also the research direction of this paper.
The first measure, (R_1^{mathcal {X}}), reports for a given clustering (text {C}in {mathcal {X}}) the fractional degree that it is the best clustering within ({mathcal {X}}) across the perturbation scenarios.
Note that the caret symbol is used also for measure (R_2^{mathcal {X}}), due to the dependence on the number of samples N. The more a clustering (text {C}) is robust within ({mathcal {X}}), the higher are the estimated values of (R_1^{mathcal {X}} text {C})) and (R_2^{mathcal {X}} text {C})).
To assess the performance of a clustering (text {C}={U_1,U_2,ldots,U_k}), obtained from the "initial" adjacency matrix (mathbf{W}_0=mathbf{W}), we propose the modified Ncut criterion as follows: (text {Ncut} text {C},mathbf{W}_I)=sum ^k_{p=1} Big (sum _{u in U_p}sum _{v notin U_p}x_{uv}/sum _{ uin U_p}sum _{v in V}x_{uv}Big )).
