81 research outputs found
Dagstuhl Reports : Volume 1, Issue 2, February 2011
Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-HĂŒbner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro PezzĂ©, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn
Peoples' lives: songs and stories, magic and law
http://www.ester.ee/record=b1147820*es
History of Psychology
Openly licensed anthology focused on the theme of the History of Psychology. Contains: The Mind and the Brain by Alfred Binet; Dream Psychology: Psychoanalysis for Beginners by Sigmund Freud; The Principles of Psychology, Volume 1 (of 2) by William James; The Principles of Psychology, Volume 2 (of 2) by William James; Collected Papers on Analytical Psychology by C. G. Jung; Memoirs of Extraordinary Popular Delusions and the Madness of Crowds by Charles Mackay; The Psychology of Arithmetic by Edward L. Thorndike
Detection and Evaluation of Clusters within Sequential Data
Motivated by theoretical advancements in dimensionality reduction techniques
we use a recent model, called Block Markov Chains, to conduct a practical study
of clustering in real-world sequential data. Clustering algorithms for Block
Markov Chains possess theoretical optimality guarantees and can be deployed in
sparse data regimes. Despite these favorable theoretical properties, a thorough
evaluation of these algorithms in realistic settings has been lacking.
We address this issue and investigate the suitability of these clustering
algorithms in exploratory data analysis of real-world sequential data. In
particular, our sequential data is derived from human DNA, written text, animal
movement data and financial markets. In order to evaluate the determined
clusters, and the associated Block Markov Chain model, we further develop a set
of evaluation tools. These tools include benchmarking, spectral noise analysis
and statistical model selection tools. An efficient implementation of the
clustering algorithm and the new evaluation tools is made available together
with this paper.
Practical challenges associated to real-world data are encountered and
discussed. It is ultimately found that the Block Markov Chain model assumption,
together with the tools developed here, can indeed produce meaningful insights
in exploratory data analyses despite the complexity and sparsity of real-world
data.Comment: 37 pages, 12 figure
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