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Search: subject:"data-driven"
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Industrial Engineering
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Sustainability
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data-driven)
2
post-hoc (a posteriori
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segmentation
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validation
2
A newly introduced product or service becomes an innovation after it has been proven in the market. No one likes the fact that market failures of products and services are much more common than commercial successes. A data-driven approach to innovation is proposed. It is a natural extension of the system of customer requirements in terms of their number and type and the ways of collecting and processing them. The ideas introduced in this paper are applicable to the evaluation of the innovativeness of planned introductions of design changes and design of new products and services. In fact
1
Data driven method
1
Generalized score test
1
Goodness of fit
1
Missing at random
1
Models and algorithms for effective decision-making in a data-driven environment are discussed. To enhance the quality of the extracted knowledge and decision-making
1
Nuisance function
1
Weighted estimating equation
1
a subset of rules is selected from the extracted knowledge to meet the established decision-making criteria. The parameter values represented by the conditions of this set of rules are called a decision signature. A model and algorithms for the selection of the desired parameters (decision signatures) will be developed. The parameters of this model are updated using a framework provided by the learning classifier systems
1
and the parameters optimizing process performance are recommended. The applications discussed in this paper differ from most data mining tasks
1
blends of products and services could be the most promising way of bringing innovations to the market. The most important toll gates of innovation are the generation of new ideas and their evaluation. People have limited ability to generate and evaluate a large number of potential innovation alternatives. The proposed approach is intended to evaluate many alternatives from a market perspective. 2009 Elsevier B.V. All rights reserved
1
cluster analysis
1
data-driven market segmentation
1
in a typical data mining application the equipment fault is recognized based on the failure symptoms. In this paper
1
the data sets are transformed
1
the impact of the decisions on the modeled process is simulated
1
the knowledge is extracted with multiple algorithms
1
where the extracted knowledge is used to assign decision values to new objects that have not been included in the training data. For example
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Dolnicar, Sara
3
Kusiak, Andrew
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Wang, Suojin
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ECONIS (ZBW)
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Innovation: A
data-driven
approach
Kusiak, Andrew
-
2009
fact that market failures of products and services are much more common than commercial successes. A
data-driven
approach …
Persistent link: https://www.econbiz.de/10009466037
Saved in:
2
Improved understanding of tourists’ needs – cross-classification for validation of
data-driven
segments
Dolnicar, Sara
-
2005
Data-driven
segmentation has become standard practice in strategic marketing. Typically, however, respondents are … decision-making: two
data-driven
segmentation solutions are constructed independently. Association between them is used as an …
Persistent link: https://www.econbiz.de/10009457365
Saved in:
3
Improved understanding of tourists’ needs – cross-classification for validation of
data-driven
segments
Dolnicar, Sara
-
2004
Data-driven
segmentation has become standard practice in strategic marketing. Typically, however, respondents are … decision-making: two
data-driven
segmentation solutions are constructed independently. Association between them is used as an …
Persistent link: https://www.econbiz.de/10009457492
Saved in:
4
Data mining and decision making
Kusiak, Andrew
-
2002
Models and algorithms for effective decision-making in a
data-driven
environment are discussed. To enhance the quality …
Persistent link: https://www.econbiz.de/10009466041
Saved in:
5
A Review of Unquestioned Standards in Using Cluster Analysis for
Data-Driven
Market Segmentation
Dolnicar, Sara
-
2002
. These goals are achieved by systematically reviewing 243
data-driven
segmentation studies that apply cluster analysis for …
Persistent link: https://www.econbiz.de/10009457616
Saved in:
6
Generalized score tests for missing covariate data
Wang, Suojin
(
contributor
)
-
2007
generalized linear models in the presence of missing covariates.The asymptotic distribution of the
data
driven
methods is provided …
Persistent link: https://www.econbiz.de/10009464824
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