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.
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Résumé, Curriculum Vitae or simply CV is an important brief about your professional life. It is likely to be one of the first contacts with a prospective employer. Curriculum Vitae means course of life in Latin. So what exactly should a Résumé contain and how detailed should it be? There is no silver bullet answer. ...
MegaMap is a Java implementation of a map (or hashtable) that can store an unbounded amount of data, limited only by the amount of disk space available. Objects stored in the map are persisted to disk. Good performance is achieved by an in-memory cache. The MegaMap can, for all practical reasons, be thought of as a map implementation with unlimited storage space.
EM has been shown to have favorable convergence properties, automatical satisfaction of constraints, and fast convergence. The next section explains the traditional approach to deriving the EM algorithm and proving its convergence property. Section 3.3 covers the interpretion the EM algorithm as the maximization of two quantities: the entropy and the expectation of complete-data likelihood. Then, the K-means algorithm and the EM algorithm are compared. The conditions under which the EM algorithm is reduced to the K-means are also explained. The discussion in Section 3.4 generalizes the EM algorithm described in Sections 3.2 and 3.3 to problems with partial-data and hidden-state. We refer to this new type of EM as the doubly stochastic EM. Finally, the chapter is concluded in Section 3.5.
In a recent piece called Strong Typing vs. Strong Testing, noted programmer and author Bruce Eckel makes an argument that dynamically typed languages such as Python are superior to statically typed languages such as Java and C++. I've done quite a bit of Python and Java programming, and even a little C++, so I can appreciate his position, but I think the conclusion goes too far. Whether Python is more productive than C++ or Java is one thing, whether static typing in general should be abandoned is quite another.
B. Dorow, and D. Widdows. Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 2, page 79--82. Morristown, NJ, USA, Association for Computational Linguistics, (2003)