By Gilles Celeux (auth.), Professor Dr. Reinhold Decker, Professor Dr. Hans -J. Lenz (eds.)
This e-book makes a speciality of exploratory facts research, studying of latent constructions in datasets, and unscrambling of data. It covers a huge diversity of tools from multivariate facts, clustering and category, visualization and scaling in addition to from facts and time sequence research. It offers new ways for info retrieval and knowledge mining. moreover, the e-book reviews hard functions in advertising and administration technology, banking and finance, bio- and overall healthiness sciences, linguistics and textual content research, statistical musicology and sound class, in addition to archaeology. particular emphasis is wear interdisciplinary learn and the interplay among thought and practice.
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Additional info for Advances in Data Analysis: Proceedings of the 30th Annual Conference of the Gesellschaft fur Klassifikation e.V., Freie Universitat Berlin, March 8–10, 2006
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28 Jos´e G. Dias Table 2. 000 A second objective of the study was the comparison of these criteria across the design factors. Increasing the sample size always improves the performance of the information criteria, and reduces underﬁtting. However, for AIC increas- Model Selection for Mixtures of Markov Chains 29 ing the sample size tends to increase the overﬁtting. Increasing the number of time points (T + 1) improves the performance of the information criteria and reduces the underﬁtting. Increasing the state space (K) reduces the underﬁtting, and improves the performance of the information criteria.