Cluster
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Cluster
As CLUSTER opens, the alien envoy Pnotl of Sphere Knyfh seeks help from Sphere Sol in a shared galactic-level crisis: Galaxy Andromeda has discovered the secret of energy transfer and intends to use it to steal the basic energy of the Milky Way Galaxy. Knyfh offers the secret of aura transfer on the understanding that Sphere Sol will spread the technology to help create a galactic coalition to find and defeat agents of Andromeda. Sol's highest-Kirlian individual is Flint, a green-skinned native of Outworld, who has a Kirlian aura of 200, an eidetic memory (useful for memorizing the complex equations of Kirlian transfer that he will need to communicate to other spheres). He has extraordinary intelligence, and is highly adaptable. His mission is complicated, however, by the fact that he is pursued everywhere by a very high Kirlian female Andromedan agent and, somehow, the Andromedans are able to detect and trace Kirlian transfers. Flint embarks upon several missions to bring transfer technology to neighboring spheres, inhabiting various alien forms. His efforts are successful despite attacks and sabotage by the Andromedan agent. Through the conflict, however, the mutual attraction of their two vastly superior auras begins to undermine their individual loyalty to their own Spheres. Flint and a group of other entities recover the information that will allow them to detect and trace transfers and one of the group is revealed as the Andromedan agent. One result is the catastrophic destruction of the local habitat. Flint and his nemesis are transferred into alien Mintakan bodies to survive. Choosing to leave things with parity between their two galaxies, Flint and the Andromedan mate and remain together until their auras fade (which happens rapidly since their physical bodies have been destroyed).
Clustering
This is the first book to take a truly comprehensive look at clustering. It begins with an introduction to cluster analysis and goes on to explore: proximity measures; hierarchical clustering; partition clustering; neural network-based clustering; kernel-based clustering; sequential data clustering; large-scale data clustering; data visualization and high-dimensional data clustering; and cluster validation. The authors assume no previous background in clustering and their generous inclusion of examples and references help make the subject matter comprehensible for readers of varying levels and backgrounds.
Identifying and ranking the world's largest clusters of inventive activity
Author: World Intellectual Property Organization
language: en
Publisher: WIPO
Release Date: 2017
This paper presents an empirical approach to identifying and ranking the world’s largest clusters of inventive activity on the basis of patent filings. Patent data offer rich information on the locality of innovative activity. Many researchers have already made use of these data to study individual clusters or selected clusters within a particular region. Our approach goes beyond existing work by identifying and ranking innovation clusters on an internationally comparable basis.