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Thus, there has been substantial development in marketing research related clustering models and methodology in the past 60 years. During the initial stages of market segmentation research in 1950s, the statistical models proposed depended heavily on existing Operations Research and Management Science methods. These models were either too complicated to be implemented practically or too unrealistic to accurately represent real-world situations. As researchers gained more computing power, models became more realistic and implementable [link] . However, current methods also have some shortcomings. For example, many segmentation models have difficulties capturing the relationship between the exogenous (consumer traits) and the response variables (ex. purchase frequencies and profits) in a segment [link] . Also, even if the model successfully relates segment traits with response, the time-dependent nature of consumer behavior is often ignored. Finally, many of the models involve only one dependent variable. Consider the purchase of printers among different groups of consumers. Such narrowly defined analysis maybe sufficient for, say a printer manufacturer, however, for an electronic store manager deciding how to stock, shelf and promote printers, it is helpful to be able to generalize the analysis to cover electronic products closely related to printers—printing paper, scanners, toners, etc.—since consumers who buy printers are likely to be interested in these products and vice versa. Thus, a multivariate segmentation approach can be more informative and useful.

Model-based clustering

A novel model-based method of clustering TSC was proposed by Thomas, Ray and Ensor [link] , who applied the method to Houston air pollution monitoring data. In the study, air quality monitor stations, represented by time series of pollution readings were clustered to identify regions of the city with similar patterns of pollution. The results from the pollution study were promising and motivate the used of model-based clustering (MBC) in other applications. In this study, our goal is to address the shortcomings of current market segmentation methods by applying MBC to consumer purchase data.

Outline for remainder of the discussion

We will explain MBC in more detail in Section 2. The description of the data is given in Section 3. Ongoing work and project implications are presented in Sections 4 and 5 respectively. Finally directions for future research are laid out in Section 6.

Methodology

The name “model-based clustering” implies two components to the method: the modeling component and the clustering component. An appropriate model is fit to each time series and then a dissimilarity metric based on the likelihood of those models is used to cluster the TCS.

Modeling tsc

A classical model for count data is Poisson regression. Recently, Fokianos and Kedem [link] proposed a model for TSC in the general linear model (GLM) framework which can be called “observation-driven” Poisson regression. In GLM, we model the response { Y t , t = 1 , , N } as a linear function of the covariates { X t , t = 1 , , N } . There are two components to a time series following a GLM:

Questions & Answers

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SUYASH
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Ebrahim
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s.
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Cied
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AMJAD
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AMJAD
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AMJAD
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Stotaw
In this morden time nanotechnology used in many field . 1-Electronics-manufacturad IC ,RAM,MRAM,solar panel etc 2-Helth and Medical-Nanomedicine,Drug Dilivery for cancer treatment etc 3- Atomobile -MEMS, Coating on car etc. and may other field for details you can check at Google
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Prasenjit Reply
At high concentrations (>0.01 M), the relation between absorptivity coefficient and absorbance is no longer linear. This is due to the electrostatic interactions between the quantum dots in close proximity. If the concentration of the solution is high, another effect that is seen is the scattering of light from the large number of quantum dots. This assumption only works at low concentrations of the analyte. Presence of stray light.
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Source:  OpenStax, The art of the pfug. OpenStax CNX. Jun 05, 2013 Download for free at http://cnx.org/content/col10523/1.34
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