Elements of information theory
Elements of information theory
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Discriminative Reranking for Natural Language Parsing
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
K-means clustering via principal component analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)
Domain adaptation of natural language processing systems
Domain adaptation of natural language processing systems
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Domain-Specific features clustering aims to cluster the features from related domains into K clusters. Although traditional clustering algorithms can be used to domain-specific features clustering, the performance may not good as the features have little inter-connection in related domains. In this paper, we develop a solution that uses the domain-independent feature as a bridge to connect the domain-specific features. And we use spectral clustering to cluster the domain-specific features into K clusters. We present theoretical analysis to show that our algorithm is able to produce high quality clusters. The experimental results show that our algorithm improves the clustering performance over the traditional algorithms.