Welcome to OSDev.org, the largest online community of operating system developers. If you want to learn how to write your own OS we have all the information to get you started. Read our OS development wiki to learn where to start. The forums are a great place to discuss OS theory and ask for help when you get stuck. Don't forget to add a link on the OS List to your OS project once it gets going.
The MCL algorithm is short for the Markov Cluster Algorithm, a fast and scalable unsupervised cluster algorithm for graphs based on simulation of (stochastic) flow in graphs.
Categories are pages that are used to group other pages on similar subjects together. This is done to help users find the pages they are looking for, even if they do not know whether it exists or what it is called.
Every page should belong to at least one category. A page may often be in several categories. However, putting a page in too many categories may not be useful.
The M-tree is an index structure that can be used for the efficient resolution of similarity queries on complex objects to be compared using an arbitrary metric
Consensus clustering has emerged as an important elaboration of the classical clustering problem. Consensus clustering, also called aggregation of clustering (or partitions), refers to the situation in which a number of different (input) clusterings have been obtained for a particular dataset and it is desired to find a single (consensus) clustering which is a better fit in some sense than the existing clusterings. Consensus clustering is thus the problem of reconciling clustering information about the same data set coming from different sources or from different runs of the same algorithm. When cast as an optimization problem, consensus clustering is known as median partition, and has been shown to be NP-complete.