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Titlebook: Data Science and Classification; Vladimir Batagelj,Hans-Hermann Bock,Aleš Žiberna Conference proceedings 2006 Springer-Verlag Berlin Heide

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Finding Meaningful and Stable Clusters Using Local Cluster Analysisovering stable and meaningful clusters. The proposed methodology is used successfully in the field of archaeometry. Here, without loss of generality, it is applied to hierarchical cluster analysis. The improvements of local cluster analysis will be illustrated by means of multivariate graphics.
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Data Science and Classification978-3-540-34416-2Series ISSN 1431-8814 Series E-ISSN 2198-3321
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1431-8814 Overview: 978-3-540-34415-5978-3-540-34416-2Series ISSN 1431-8814 Series E-ISSN 2198-3321
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Frank M. Dattilio,Robert J. Berchicko compare the performance of different information criteria for this model, some compared for the first time. Furthermore, the level of separation of latent classes is controlled using a new procedure. The results show that AIC3 (Akaike information criterion with 3 as penalizing factor) has a balanced performance for binary LC models.
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Maria Vamvini,Florence M. Browning applicants as either suitable or unsuitable for a job. Our main result specifies the condition that determines the number of predictor tests, the collective assessment rule (aggregation procedure of predictor tests’ recommendations) and the function relating the tests’ assessment skills to the predictor cutoff points.
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