TAILIEUCHUNG - Data Mining and Knowledge Discovery Handbook, 2 Edition part 98

Data Mining and Knowledge Discovery Handbook, 2 Edition part 98. 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. | 950 Hong Yao Cory J. Butz and Howard J. Hamilton shown that FD logically implies CI Butz et al. 1999 . We show how to combine the obtained FDs with the chain rule of probability to construct a DAG of a CN. Given a set of FDs obtained from data an ordering of variables is obtained such that the Markov boundaries of some variables are determined. Representing joint probability distribution of variables in the resulting ordering by the chain rule the Markov boundaries of the other variables are determined. A DAG of a CN is constructed by designating each Markov boundary of variable as the parent set of the variable. During this process we take full advantage of known results in both CNs Pearl 1988 and relational databases Maier 1983 . We demonstrate the effectiveness of our approach using fifteen real-world datasets. The DAG constructed in our approach can also be used as an initial DAG for previous approaches. The work here further illustrates the intrinsic relationship between CNs and relational databases Wong et al. 2000 Wong and Butz 2001 . The remainder of this chapter is organized as follows. Background knowledge is given in Section . In Section the theoretical foundation of our approach is provided. The algorithm to construct a CN is developed in Section . In Section the experimental results are presented. Conclusions are drawn in Section . Background Knowledge Let U be a finite set of discrete variables each with a finite domain. Let V be the Cartesian product of the variable domains. A joint probability distribution Pearl 1988 p U is a function p on V such that 0 p v 1 for each configuration v E V and veVp v . The marginal distribution p X for X C U is defined as u-xp U . If p X 0 then the conditional probability distribution p Y X for X Y C U is defined as p XY p X . In this chapter we may write ai for the singleton set ai and we use the terms attribute and variable interchangeably. Similarly for the terms tuple and .

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