Advances in data analysis: proceedings of the 30th Annual by Reinhold Decker, Hans-Joachim Lenz

By Reinhold Decker, Hans-Joachim Lenz

The publication makes a speciality of exploratory facts research, studying of latent buildings in datasets, and unscrambling of information. It covers a large variety of tools from multivariate statistics, clustering and category, visualization and scaling in addition to from facts and time sequence research. It presents new techniques for info retrieval and information mining. moreover, the e-book experiences hard purposes in advertising and administration technological know-how, banking and finance, bio- and healthiness sciences, linguistics and textual content research, statistical musicology and sound category, in addition to archaeology. unique emphasis is wear interdisciplinary study and the interplay among concept and perform.

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By Reinhold Decker, Hans-Joachim Lenz

The publication makes a speciality of exploratory facts research, studying of latent buildings in datasets, and unscrambling of information. It covers a large variety of tools from multivariate statistics, clustering and category, visualization and scaling in addition to from facts and time sequence research. It presents new techniques for info retrieval and information mining. moreover, the e-book experiences hard purposes in advertising and administration technological know-how, banking and finance, bio- and healthiness sciences, linguistics and textual content research, statistical musicology and sound category, in addition to archaeology. unique emphasis is wear interdisciplinary study and the interplay among concept and perform.

Show description

Read or Download Advances in data analysis: proceedings of the 30th Annual Conference of The Gesellschaft fur Klassifikation e.V., Freie Universitat Berlin, March 8-10, 2006 PDF

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Extra resources 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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5 Results The key feature of the results is the overall remarkable performance of AIC3 (Table 2). 9% of the times. 6%), and therefore outperforms other traditional criteria such as AIC, BIC, and CAIC. 2%). For CAIC, BIC, and AWE the penalization n(T + 1) decreases their performance and it is not considered hereafter. 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.

1. Example of data sampled from three different gaussian distributions. gories of sets. The first category is classical for the clustering analysis, we give two sets as examples. mat). The plot in Figure 3 corresponds to a mixture of five Gaussian distributions generated by x = mx + R cos U and y = my + R sin U where (mx , my ) is the local mean point chosen from the set {(3, 18), (3, 9), (9, 3), (18, 9), (18, 18)}. R and U are random variables distributed N ormal(0, 1) and U nif orm(0, π) respectively.

The main reason for this is lack of real symbolic datasets with known data structure. There are only a few datasets shipped with the SODAS Software. But we can assume that switching from artificial to real data wouldn’t change the results of the simulation, as far as the real cluster sizes are approximately equal. For datasets with one “large” and few “small” clusters the situation probably differs. Each data set contained a fixed number of objects (150), a random number (from 2 to 5) of single numerical variables, a random number (from 2 to 10) of variables in form of intervals and a random number (from 2 to 10) of multi-nominal variables.

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