Data objects in a relational database are cross-linked with each other via multi-typed links. Links contain rich semantic information that may indicate important relationships among objects, such as the similarities between objects. In this chapter we explore linkage-based clustering, in which the similarity between two objects is measured based on the similarities between the objects linked with them. We study a hierarchical structure called SimTree, which represents similarities in multi-granularity manner. This method avoids the high cost of computing and storing pairwise similarities but still thoroughly explore relationships among objects. We introduce an efficient algorithm for computing similarities utilizing the SimTree.
|Original language||English (US)|
|Title of host publication||Link Mining|
|Subtitle of host publication||Models, Algorithms, and Applications|
|Publisher||Springer New York|
|Number of pages||27|
|State||Published - Jan 1 2010|
ASJC Scopus subject areas