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This booklet makes a speciality of exploratory info research, studying of latent constructions in datasets, and unscrambling of information. insurance info a extensive variety of tools from multivariate facts, clustering and class, visualization and scaling in addition to from information and time sequence research. It presents new methods for info retrieval and knowledge mining and reviews a number of hard purposes in a number of fields.
Read or Download Advances in Data Analysis: Proceedings of the 30th Annual Conference of the Gesellschaft fur Klassifikation e.V., Freie Universitat Berlin, March ... Data Analysis, and Knowledge Organization) PDF
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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 ... Data Analysis, and Knowledge Organization)
For diﬀerent values of d, we have the Akaike Information Criterion (AIC: Akaike (1974)) (d = 2), the Bayesian Information Criterion (BIC: Schwarz (1978)) (d = log n), and the Consistent Akaike Information Criterion (CAIC: Bozdogan (1987)) (d = log n + 1). 26 Jos´e G. Dias Bozdogan (1993) argues that the marginal cost per free parameter, the socalled magic number 2 in AIC’s equation above, is not correct for ﬁnite mixture models. Based on Wolfe (1970), he conjectures that the likelihood ratio for comparing mixture models with p1 and p2 free parameters is asymptotically distributed as a noncentral chi-square with noncentrality parameter δ and 2(p1 − p2 ) degrees of freedom instead of the usual p1 − p2 degrees of freedom as assumed in AIC.
6 Final remarks In this paper several cluster quality indexes were compared on 100 artiﬁcially generated symbolic data sets. The experiment showed that the most adequate ones for this kind of data are the Hubert and Levine and the Baker and Hubert indexes. We can assume that the usage of these indexes in case of real symbolic data validation should also give good results. The preliminary experiments with real symbolic data sets, done by the author, also conﬁrm the quality of these indexes in the symbolic data case.
The Average Weight of Evidence (AWE) criterion adds a third dimension to the information criteria described above. It weights ﬁt, parsimony, and the performance of the classiﬁcation (Banﬁeld and Raftery (1993)). This measure uses the so-called classiﬁcation log-likelihood (log Lc ) and is deﬁned as AW E = −2 log Lc + 2NS ( 23 + log n). Apart from the ﬁve information criteria reported above, we also investigated a modiﬁed deﬁnition of the BIC, CAIC and AWE. , Ramaswamy et al. (1993), DeSarbo et al.