The formation and use of abstract concepts in design
Concept formation knowledge and experience in unsupervised learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Category learning through multimodality sensing
Neural Computation
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Synthesizing High-Frequency Rules from Different Data Sources
IEEE Transactions on Knowledge and Data Engineering
Exploiting Context When Learning to Classify
ECML '93 Proceedings of the European Conference on Machine Learning
Combining clustering and co-training to enhance text classification using unlabelled data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Mining Multiple Data Sources: Local Pattern Analysis
Data Mining and Knowledge Discovery
Heterogeneous source consensus learning via decision propagation and negotiation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Application of a generalization of russo's formula to learning from multiple random oracles
Combinatorics, Probability and Computing
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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.