R.A. Dara, T. Khan, J. Azim, O. Cicchello, and G. Cort (Canada)
Semi-supervised learning; Customer Relationship Management; Classification; Clustering; Labeled Data; Unlabeled Data
With the increase of customer information and the rapid change of customer requirements, the need for automated intelligent systems is becoming more vital. An automated system reduces human intervention, improves the quality of information extracted, and provides fast feedback for decision making purposes. This study investigates the use of a technique, semi-supervised learning, for the management and analysis of customer-related databases and information. The proposed semi-supervised method is a hybrid neural network algorithm in which the output of a clustering algorithm is used to assign labels to records in a database. The newly classified records will later be used to train a classification algorithm in order to predict the category of an unknown record. The advantage of this technique is that the process of obtaining large amount of training data is automatic, which results in saving time, labor, and increased productivity. In addition, this technique can be used for both classification and clustering (descriptive and predictive analysis) purposes.
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