Co-clustering for queries and corresponding advertisement

• Published in: 2009 International Conference on Machine Learning and Cybernetics Vol. 4; pp. 2296 - 2299
• Main Authors: Fan Yang, Bin An, Xizhao Wang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2009
• Subjects: Advertising; Clustering algorithms; Co-Clustering; Computer science; Cybernetics; DBSCAN; Intrusion detection; K-mean clustering; Machine learning; Machine learning algorithms; Mathematics; Online advertisement; Query; Random variables; Search engines; Singular Value Decomposition
• ISBN: 9781424437023; 1424437024
• ISSN: 2160-133X
• DOI: 10.1109/ICMLC.2009.5212131

Both documents clustering and words clustering are well studied problems. Most existing algorithms cluster documents (advertisement) and words (query) separately but not simultaneously. In this paper we present a novel idea of analyzing both queries and advertisements which occur with queries at the same time. We present an innovative co-clustering algorithm that suggests queries by co-clustering advertisements and queries. We pose the co-clustering problem as an optimization problem in information theory - the optimal co-clustering maximizes the mutual information between the clustered random variables subject to constraints on the number of row and column clusters.