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Applying text mining and semantic network analysis to investigate effects of perceived crowding in the service sector

Ellahi, Abida; Ain, Qurat Ul; Rehman, Hafiz Mudassir; … - In: Cogent Business & Management 10 (2023) 2, pp. 1-18

approach in the service sector. The data collected was analyzed using a text mining approach in Python library and Ucinet … software. The text data collected through interviews was analyzed using multiple techniques like sentiment analysis, centrality … responses. The study offers important implications of text analysis for business intelligence. …

Persistent link: https://www.econbiz.de/10014527941

Testing the significance of calendar effects

Hansen, Peter Reinhard; Lunde, Asger - 2003

When evaluating the significance of calendar effects, such as those associated with Monday and January, it is necessary to control for all possible calendar effects to avoid spurious results. The downside of having to control for a large number of possible calendar effects is that it diminish...

Persistent link: https://www.econbiz.de/10010318856

Stock picking via nonsymmetrically pruned binary decision trees

Andriyashin, Anton - 2008

Stock picking is the field of financial analysis that is of particular interest for many professional investors and researchers. In this study stock picking is implemented via binary classification trees. Optimal tree size is believed to be the crucial factor in forecasting performance of the...

Persistent link: https://www.econbiz.de/10010263732

Insurance: an R-Program to Model Insurance Data

Marin-Galiano, Marcos; Christmann, Andreas - 2004

Data sets from car insurance companies often have a high-dimensional complex dependency structure. The use of classical statistical methods such as generalized linear models or Tweedie?s compound Poisson model can yield problems in this case. Christmann (2004) proposed a general approach to...

Persistent link: https://www.econbiz.de/10010296633

Robust Learning from Bites for Data Mining

Christmann, Andreas; Steinwart, Ingo; Hubert, Mia - 2006

Some methods from statistical machine learning and from robust statistics have two drawbacks. Firstly, they are computer-intensive such that they can hardly be used for massive data sets, say with millions of data points. Secondly, robust and non-parametric confidence intervals for the...

Persistent link: https://www.econbiz.de/10010296722

Data Mining Projekte im unternehmerischen Umfeld : eine empirische Studie deutscher Unternehmen

Hilbert, Andreas - 2002

In dieser Studie werden zunächst die konzeptionellen Grundlagen des Customer Relationship Managements und der sog. CRM-Systeme dargestellt sowie die Einbettung von Data Warehouse und Data Mining Technologie in diesem Kontext erörtert. Danach erfolgt eine Literatur gestützte Diskussion...

Persistent link: https://www.econbiz.de/10010296833

Analyse zeitabhängiger Daten durch Data-Mining-Verfahren

Wissuwa, Stefan; Cleve, Jürgen; Lämmel, Uwe - 2005

Persistent link: https://www.econbiz.de/10010297060

Data Mining mittels künstlicher neuronaler Netze

Lämmel, Uwe - 2003

Persistent link: https://www.econbiz.de/10010301389

On a strategy to develop robust and simple tariffs from motor vehicle insurance data

Christmann, Andreas - 2004

The goals of this paper are twofold: we describe common features in data sets from motor vehicle insurance companies and we investigate a general strategy which exploits the knowledge of such features. The results of the strategy are a basis to develop insurance tariffs. The strategy is applied...

Persistent link: https://www.econbiz.de/10010306241

A patent search strategy based on machine learning for the emerging field of service robotics

Kreuchauff, Florian; Korzinov, Vladimir - 2015

Emerging technologies are in the core focus of supra-national innovation policies. These strongly rely on credible data bases for being effective and efficient. However, since emerging technologies are not yet part of any official industry, patent or trademark classification systems, delineating...

Persistent link: https://www.econbiz.de/10011341069

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