tailieunhanh - Data Mining and Knowledge Discovery Handbook, 2 Edition part 21

Data Mining and Knowledge Discovery Handbook, 2 Edition part 21. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 180 Paola Sebastiani Maria M. Abad and Marco F. Ramoni Several exact algorithms exist to perform this inference when the network variables are all discrete all continuous and modeled with Gaussian distributions or the network topology is constrained to particular structures Castillo et al. 1997 Lauritzen and Spiegelhalter 1988 Pearl 1988 . The most common approaches to evidence propagation in Bayesian networks can be summarized along four lines Polytrees When the topology of a Bayesian network is restricted to a polytree structure a direct acyclic graph with only one path linking any two nodes in the graph we can the fact that every node in the network divides the polytree into two disjoint sub-trees. In this way propagation can be performed locally and very efficiently. Conditioning The intuition underlying the Conditioning approach is that networks structures more complex than poly trees can be reduced to a set of poly trees when a subset of its nodes known as loop cutset are instantiated. In this way we can efficiently propagate each polytree and then combine the results of these propagations. The source of complexity of these algorithms is the identification of the loop cutset Cooper 1990 . Clustering The algorithms developed following the Clustering approach Lauritzen and Spiegelhalter 1988 transforms the graphical structure of a Bayesian network into an alternative graph called the junction tree with a polytree structure by appropriately merging some variables in the network. This mapping consists first of transforming the directed graph into an undirected graph by joining the unlinked parents and triangulating the graph. The nodes in the junction tree cluster sets of nodes in the undirected graph into cliques that are defined as maximal and complete sets of nodes. The completeness ensures that there are links between every pair of nodes in the clique while maximality guarantees that the set on nodes is not a proper subset of any other clique. The joint .