Clustering and Graph Analysis in Data Streams. Given the importance of clustering as a basic tool for analyzing massive data sets, it is unsurprising that considerable effort has gone into designing clustering algorithms in the relevant computational models. In particular, in the data-stream model ...
BMC Bioinformatics (2023) 24:454 Page 3 of 15 Mstcom [22] introduces the concept of Hamming-shifting graphs to encode similar redundant reads and employ compressors BSC (http://libbsc.com) and LZMA (http:// www.7zip.org) for compressing the encoded file streams. Like ...
Using P2P networks, it is possible to share the resources and thus reduce the requirement for high-end hardware (Bashmal et al., 2017). The P2P networks improve the parallelism to enhance the clustering process speed. Communication in P2P networks can be expensive if the traffic is with a ...
This paper focuses on one representative of a class of these data structures, namely one based on clustering for which we evaluate different ways of distributing the index to support parallelism on a set of processors. Our study reveals that the intuitive method for both data distribution and ...
A key factor in the two algorithms is their parallel implementation in Java, based on functional programming using streams and lambda expressions. The use of parallelism smooths out the O (N 2) computational cost behind K-medoids and clustering indexes such as the Silhouette index and allows for...