TY - BOOK ID - 7838660 TI - Link Mining: Models, Algorithms, and Applications AU - Yu, Philip S. AU - Han, Jiawei. AU - Faloutsos, Christos. PY - 2010 SN - 1493901478 1441965149 9786612979552 1441965157 1282979558 PB - New York, NY : Springer New York : Imprint: Springer, DB - UniCat KW - Data mining. KW - Marketing -- Data processing. KW - Data mining KW - Biology KW - Engineering & Applied Sciences KW - Health & Biological Sciences KW - Computer Science KW - Biology - General KW - Database searching. KW - Data base searching KW - Database search strategies KW - Search strategies in databases KW - Searching databases KW - Algorithmic knowledge discovery KW - Factual data analysis KW - KDD (Information retrieval) KW - Knowledge discovery in data KW - Knowledge discovery in databases KW - Mining, Data KW - Life sciences. KW - Bioinformatics. KW - Computational biology. KW - Life Sciences. KW - Data Mining and Knowledge Discovery. KW - Computational Biology/Bioinformatics. KW - Computer Appl. in Life Sciences. KW - Database searching KW - Electronic information resource searching KW - Data processing. KW - Bio-informatics KW - Biological informatics KW - Information science KW - Computational biology KW - Systems biology KW - Data processing KW - Bioinformatics . KW - Computational biology . KW - Bioinformatics UR - https://www.unicat.be/uniCat?func=search&query=sysid:7838660 AB - With the recent flourishing research activities on Web search and mining, social network analysis, information network analysis, information retrieval, link analysis, and structural data mining, research on link mining has been rapidly growing, forming a new field of data mining. Traditional data mining focuses on "flat" or “isolated” data in which each data object is represented as an independent attribute vector. However, many real-world data sets are inter-connected, much richer in structure, involving objects of heterogeneous types and complex links. Hence, the study of link mining will have a high impact in various important applications such as Web and text mining, social network analysis, collaborative filtering, and bioinformatics. Link Mining: Models, Algorithms and Applications focuses on the theory and techniques as well as the related applications for link mining, especially from an interdisciplinary point of view. Due to the high popularity of linkage data, extensive applications ranging from governmental organizations to commercial businesses to people's daily life call for exploring the techniques of mining linkage data. This book provides a comprehensive coverage of the link mining models, techniques and applications. Each chapter is contributed from some well known researchers in the field. Link Mining: Models, Algorithms and Applications is designed for researchers, teachers, and advanced-level students in computer science. This book is also suitable for practitioners in industry. ER -