215 research outputs found
Some Remarks about the Usage of Asymmetric Correlation Measurements for the Induction of Decision Trees
Decision trees are used very successfully for the identification resp. classification task of objects in many domains like marketing (e.g. Decker, Temme (2001)) or medicine. Other procedures to classify objects are for instance the logistic regression, the logit- or probit analysis, the linear or squared discriminant analysis, the nearest neighbour procedure or some kernel density estimators. The common aim of all these classification procedures is to generate classification rules which describe the correlation between some independent exogenous variables resp. attributes and at least one endogenous variable, the so called class membership variable. --
Modeling the structure and evolution of discussion cascades
We analyze the structure and evolution of discussion cascades in four popular
websites: Slashdot, Barrapunto, Meneame and Wikipedia. Despite the big
heterogeneities between these sites, a preferential attachment (PA) model with
bias to the root can capture the temporal evolution of the observed trees and
many of their statistical properties, namely, probability distributions of the
branching factors (degrees), subtree sizes and certain correlations. The
parameters of the model are learned efficiently using a novel maximum
likelihood estimation scheme for PA and provide a figurative interpretation
about the communication habits and the resulting discussion cascades on the
four different websites.Comment: 10 pages, 11 figure
CUSTOMER-CENTRIC REVENUE MANAGEMENT IN MANUFACTURING - A DECISION SUPPORT SYSTEM
Manufacturing providers aim not only for a revenu maximizing allocation of their limited production capacity but also for the establishment of long-term customer relations. Du to long-term contracts and strategic reference customers, users of traditional revenu management systems already account for varying worthiness of clients, and intuitively ignore or override booking control suggestions, such as orderÂŽs denial or pricing level, in order not to endanger customer relations. So with a view to a holistic approach, the integration of both management concepts, each of decisive competitive impact, is advised. However, an implemented IT-system, that provides the revenu analyst with greater insights, higher accuracy, quality and trust in decision process, is still missing in manufacturing industry. This reflects the common frustration of managers and analysts in practice when dealing with conflicting ideas or theories generated by research community. We believe our prototype is the first to supply analysts with formatted and summarized information to make transparent and comprehensible control decisions, suggesting specific booking control actions based on simulation results and integrated usage of provided data. It also accounts for the strategic dimension of the problem when confronted with these partly diametric objectives of revenu vs. customer relationship management
Analyzing customer sentiments in microblogs â A topic-model-based approach for Twitter datasets
In the Social Commerce customers evolve to an important information source for companies. The customers use communication platforms of the Web 2.0, for example Twitter, in order to express their opinions about products or discuss their experiences with them. These opinions can be very important for the development of products or the product range of a company. Our approach enables a company viewing opinions about its products which are published using the microblogging service Twitter. A first step in our research progress is detecting topics in a specific context. In a further step the entries corresponding to these topics has to be analyzed for opinions. For topic detection we use topic modeling with the Latent Dirichlet Allocation. In our paper we found event-based topics in the context of Sonyâs 3D TV sets. In future work we are able to implement Opinion Mining algorithms to determine sentiments in the entries corresponding to the detected topics
CONCEPTUAL MODEL AND OPERATIONAL PROCESSES OF CUSTOMER VALUE-BASED REVENUE MANAGEMENT IN TRANSPORT AND LOGISTICS
The approach presented in this article addresses the shortcomings of transaction-based revenue management and proposes a conceptual model of customer value-based revenue management to allow for both an efficient utilization of limited capacity resources and the establishment of profitable customer relationships. Furthermore, process models are developed for the operational tasks as well as results of a prototypical implementation are presented. Finally, some concluding remarks and an outlook on remaining research are given
Idea Mining â Text Mining Supported Knowledge Management for Innovation Purposes
Following the emergence of Social Media and the increasing willingness of customers to share thoughts, ideas, and experiences companies are trying to capitalize on such activities. Due to the vast amount of user-generated content, manual analysis and interpretation will not meet the demands of companies in highly competitive environments. Based on an integrative process model, which describes the process of idea generation, we outline a BPMN-based path that allows companies to steer user participation and the application of Text Mining methods to gain valuable ideas for innovative products. Our approach also illustrates the Knowledge Management perspective supporting the customers during idea generation. In order to demonstrate the applicability of our model we finally depict the whole process utilizing Dellâs IdeaStor
Entwicklung eines generischen Vorgehensmodells fĂŒr Text Mining
Vor dem Hintergrund des steigenden Interesses von computergestĂŒtzter Textanalyse in Forschung und Praxis entwickelt dieser Beitrag auf Basis aktueller Literatur ein generisches Vorgehensmodell fĂŒr Text-Mining-Prozesse. Das Ziel des Beitrags ist, die dabei anfallenden, umfangreichen AktivitĂ€ten zu strukturieren und dadurch die KomplexitĂ€t von Text-Mining-Vorhaben zu reduzieren. Das Forschungsziel stĂŒtzt sich auf die Tatsache, dass im Rahmen einer im Vorfeld durchgefĂŒhrten, systematischen Literatur-Review keine detaillierten, anwendungsneutralen Vorgehensmodelle fĂŒr Text Mining identifiziert werden konnten. Aufbauend auf den Erkenntnissen der Literatur-Review enthĂ€lt das resultierende Modell daher sowohl induktiv begrĂŒndete Komponenten aus spezifischen AnsĂ€tzen als auch aus literaturbasierten Anforderungen deduktiv abgeleitete Bestandteile. Die Evaluation des Artefakts belegt die NĂŒtzlichkeit des Vorgehensmodells im Vergleich mit dem bisherigen Forschungsstand.:1 EinfĂŒhrung
1.1 Motivation
1.2 Forschungsziel und Methodik
1.2.1 Systematische Literatur-Review
1.2.2 Design-Science-Research-Ansatz
1.3 Aufbau des Beitrags
2 Stand der Forschung
2.1 BegriffsverstÀndnis
2.2 Merkmale von Vorgehensmodellen fĂŒr Text Mining
2.3 AktivitÀten im Text-Mining-Prozess
2.4 Zusammenfassung
3 Anforderungen an ein generisches Vorgehensmodell
3.1 Strukturelle Anforderungen
3.2 Funktionelle Anforderungen
3.3 Zusammenfassung
4 Entwicklung des Modells
4.1 Aufgabendefinition
4.2 Dokumentenselektion und -untersuchung
4.3 Dokumentenaufbereitung
4.3.1 Linguistische Aufbereitung
4.3.2 Technische Aufbereitung
4.4 Text-Mining-Verfahren
4.5 Ergebnisevaluation
4.6 Anwendung
4.7 Zusammenfassung
4.7.1 Gesamtmodell
4.7.2 Feedbackschleifen
5 Evaluation
5.1 Evaluationsdesign
5.2 Messung und Auswertung
6 Fazit und Ausblick
Literaturverzeichnis
Anhang
A1 Anwendungsneutrale Vorgehensmodelle
A2 Auswirkungen von Grund- und Stammformenreduktion auf die Interpretierbarkeit von Texten
A3 Gesamtmodel
Status Quo der Textanalyse im Rahmen der Business Intelligence
Vor dem Hintergrund der Zunahme unstrukturierter Daten fĂŒr Unternehmen befasst sich dieser Beitrag mit den Möglichkeiten, die durch den Einsatz der Business Intelligence fĂŒr Unternehmen bestehen, wenn durch gezielte Analyse die Bedeutung dieser Daten erfasst, gefiltert und ausgewertet werden können. Allgemein ist das Ziel der Business Intelligence die UnterstĂŒtzung von Entscheidungen, die im Unternehmen (auf Basis strukturierter Daten) getroffen werden. Die zusĂ€tzliche Auswertung von unstrukturierten Daten, d.h. unternehmensinternen Dokumenten oder Texten aus dem Web 2.0, fĂŒhrt zu einer VergröĂerung des Potenzials und dient der Erweiterung des GeschĂ€ftsverstĂ€ndnisses der Verbesserung der Entscheidungsfindung. Der Beitrag erlĂ€utert dabei nicht nur Konzepte und Verfahren, die diese Analysen ermöglichen, sondern zeigt auch Fallbeispiele zur Demonstration ihrer NĂŒtzlichkeit.:1 EinfĂŒhrung
2 Business Intelligence
2.1 Definition
2.2 Ordnungsrahmen
2.3 Analyseorientierte BI und Data Mining
3 Text Mining
3.1 BerĂŒhrungspunkte mit anderen Disziplinen
3.2 Definition
3.3 Prozessmodell nach HIPPNER & RENTZMANN (2006a)
3.3.1 Aufgabendefinition
3.3.2 Dokumentselektion
3.3.3 Dokumentaufbereitung
3.3.4 Text-Mining-Methoden
3.3.5 Interpretation / Evaluation
3.3.6 Anwendung
4 Potenziale der Textanalyse
4.1 Erweiterung des CRM
4.2 Alternative zur Marktforschung
5 Fazit und Ausblick
Literaturverzeichni
Interaction Effects of Child Weight Status and Parental Feeding Practices on Childrenâs Eating Disorder Symptomatology
(1) Background: Research on parental feeding practices and non-normative eating
behavior including loss of control (LOC) eating and eating disorder psychopathology indicated
separate associations of these variables with child weight status, especially in early childhood.
This study cross-sectionally examined interaction effects of restriction, monitoring, pressure to
eat, and childrenâs weight status on disordered eating in children aged 8â13 years. (2) Methods:
A population-based sample of N = 904 children and their mothers completed the Eating Disorder
Examination Questionnaire for Children and the Child Feeding Questionnaire. Child anthropometrics
were objectively measured. Hierarchical linear and logistic regression analyses were conducted
for cross-sectionally predicting global eating disorder psychopathology and recurrent LOC eating
by feeding practices and child weight status for younger (8â10 years) and older (11â13 years)
ages. (3) Results: Restriction x Child weight status significantly predicted global eating disorder
psychopathology in younger children and recurrent LOC eating in older children. Monitoring x Child
weight status significantly predicted eating disorder psychopathology in older children. A higher
versus lower child weight status was associated with adverse eating behaviors, particularly in children
with mothers reporting high restriction and monitoring. (4) Conclusions: Detrimental associations
between higher child weight status and child eating disorder symptomatology held especially true
for children whose mothers strongly control child food intake
Data Mining Projekte im unternehmerischen Umfeld : eine empirische Studie deutscher Unternehmen
In dieser Studie werden zunĂ€chst 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 gestĂŒtzte Diskussion möglicher Erfolgsdeterminanten von CRM-Systemen in Hinblick au den Einsatz von Data Mining. Die daraus abgeleiteten Hypothesen bzgl. kritischer Erfolgsfaktoren des Data Mining sollen dann im Rahmen einer empirischen Studie genauer analysiert werden. Dazu werden die Ergebnisse der Studie uni-, bi- und multivariat deskriptiv ausgewertet sowie konfirmatorisch zur Validierung der Thesen im Rahmen einer Kausalanalyse ĂŒberprĂŒft. AbschlieĂend sollen noch Handlungsempfehlungen gegeben werden, die Unternehmen helfen sollen, die Implementierung und Umsetzung von Data Mining Technologien zielgerichtet voranzutreiben. --
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