استخراج ویژگی های زمینه ای معنی دار و لغو سر و صدا با استفاده از تجزیه و تحلیل مولفه های مستقل برای بازاریابی مستقیم
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|2407||2007||11 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 33, Issue 1, July 2007, Pages 181–191
As the Internet spreads widely, it has become easier for companies to obtain and utilize valuable information on their customers. Nevertheless, many of them have difficulty in using the information effectively because of the huge amount of data from their customers that must to be analyzed. In addition, the data usually contains much noise due to anonymity of the Internet. Consequently, extracting the underlying meanings and canceling the noise of the collected customer data are crucial for the companies to implement their strategies for customer relationship management. As a novel solution, we propose the use of independent component analysis (ICA). ICA is a multivariate statistical tool which extracts independent components or sources of information, given only observed data that are assumed to be linear mixtures of some unknown sources. Moreover, ICA is able to reduce the dimension of the observed data, especially noisy variables. To validate the usefulness of ICA, we applied it to a real-world one-to-one marketing case. In this study, we used ICA as a preprocessing tool, and made a prediction for potential buyers using artificial neural networks (ANNs). We also applied PCA as a comparative model for ICA. The experimental results showed that ICA-preprocessed ANN outperformed all the comparative classifiers without preprocessing as well as PCA-preprocessed ANN.