Discovering Classification from Data of Multiple Sources

In many large e-commerce organizations, multiple data sources are often used to describe the same customers, thus it is important to consolidate data of multiple sources for intelligent business decision making. In this paper, we propose a novel method that predicts the classification of data from multiple sources without class labels in each source. We test our method on artificial and real-world datasets, and show that it can classify the data accurately. From the machine learning perspective, our method removes the fundamental assumption of providing class labels in supervised learning, and bridges the gap between supervised and unsupervised learning.

• Type: Journal
• Year: 2006
• Where: DATAMINE
• Authors: Charles X. Ling, Qiang Yang
• Keywords: DATAMINE 2006, Large E-commerce Organizations, Multiple Data Sources, Multiple Sources