Abstract
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.
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The integer in parentheses (for example 384) means there are 384 instances in this leaf or cluster. All the trees in the paper are represented in the same format as the output of C4.5 (Quinlan, 1993).
Normally the partition trees are different from (and larger than) the ideal ones, as shown in later subsections on incomplete and noisy datasets.
This is suggested by Doug Fisher.
These two datasets have a large number of discrete attributes. Recall that CMS currently works only on discrete attributes.
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Acknowledgments
We thank Doug Fisher and Joel Martin for their extensive and insightful comments and suggestions on the earlier versions of the paper. We also thank Chenghui Li for discussions and working with CMS. Qiang Yang thanks the support of Hong Kong RGC grant HKUST 6187/04E.
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Ling, C.X., Yang, Q. Discovering Classification from Data of Multiple Sources. Data Min Knowl Disc 12, 181–201 (2006). https://doi.org/10.1007/s10618-005-0013-7
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DOI: https://doi.org/10.1007/s10618-005-0013-7