Centrality and Diversity in Search: Roles in A.I., Machine Learning, Social Networks, and Pattern Recognition

Description

The concepts of centrality and diversity are highly important in search algorithms, and play central roles in applications of artificial intelligence (AI), machine learning (ML), social networks, and pattern recognition. This work examines the significance of centrality and diversity in representation, regression, ranking, clustering, optimization, and classification. The text is designed to be accessible to a broad readership. Requiring only a basic background in undergraduate-level mathematics, the work is suitable for senior undergraduate and graduate students, as well as researchers working in machine learning, data mining, social networks, and pattern recognition. Front Matter ....Pages i-xi Introduction (M. N. Murty, Anirban Biswas)....Pages 1-12 Searching (M. N. Murty, Anirban Biswas)....Pages 13-28 Representation (M. N. Murty, Anirban Biswas)....Pages 29-47 Clustering and Classification (M. N. Murty, Anirban Biswas)....Pages 49-63 Ranking (M. N. Murty, Anirban Biswas)....Pages 65-69 Centrality and Diversity in Social and Information Networks (M. N. Murty, Anirban Biswas)....Pages 71-86 Conclusion (M. N. Murty, Anirban Biswas)....Pages 87-87 Back Matter ....Pages 89-94