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Tech Report kmi-01-18 Abstract


Hierarchical clustering speed up using position lists and data position hierarchy
Techreport ID: kmi-01-18
Date: 2001
Author(s): Jiri Komzak
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The aim of this paper is to address the nature of hierarchical clustering problems in systems with very large numbers of entities, and to propose specific speed improvements in the clustering algorithm. The motivation for this theme arises from the challenge of visualising the geographic and logical distribution of many tens of thousands of distance-learning students at the UK's Open University. A general algorithm for solving hierarchical clustering is mentioned at the beginning. Then the paper describes (i) a speed-up technique based on lists sorted according to particular dimensions or attributes of the entities to be visualised and (ii) a speed-up technique based upon hierarchical partitioning into regions. At the end, the paper discusses the algorithm's complexity and presents experimental results. Keywords hierarchical clustering, position hierarchy, position list, geographical information system
 
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Social Software is...


Social Software
Social Software can be thought of as "software which extends, or derives added value from, human social behaviour - message boards, musical taste-sharing, photo-sharing, instant messaging, mailing lists, social networking."

Interacting with other people not only forms the core of human social and psychological experience, but also lies at the centre of what makes the internet such a rich, powerful and exciting collection of knowledge media. We are especially interested in what happens when such interactions take place on a very large scale -- not only because we work regularly with tens of thousands of distance learners at the Open University, but also because it is evident that being part of a crowd in real life possesses a certain 'buzz' of its own, and poses a natural challenge. Different nuances emerge in different user contexts, so we choose to investigate the contexts of work, learning and play to better understand the trade-offs involved in designing effective large-scale social software for multiple purposes.