Data Mining and Knowledge Discovery Handbook, 2 Edition part 118. 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. | 1150 Gautam B. Singh probability density function. This is accomplished by associating the cell probability value Pij defined in Eq. . pij CC 59-17 L Clip 9 1 N In the final step the uncertainty of finding a pattern B given that a pattern A is present is defined by Eq. . L PBt lnPBi-PAB lnPAB U BA H B h HBIa i L B n .----- If the presence of a pattern A results in a low value for the uncertainty that the pattern B is present then we have a meta-pattern. Figure shows the MAR and the transcription factor analysis of Chromosome I for S. cerevisea. A correlation between the high density of transcription factor binding sites and the matrix attachment regions is evident in this plot. This plot will assist in identifying regions further biological investigation. Fig. . A cumulative analysis of yeast Chromosome I using MAR detection algorithm and isolation of transcription density regions. Conclusions In this chapter we described the process for learning stochastic models of known lower-level patterns and using them in an inductive procedure to learn meta-pattern organization. The next logical step is to extend this unsupervised learning process to include lower level patterns that have not yet been discovered and thus not included in the pattern sets available within the databases such as such as TFD TRANSFAC EPD. In this case our analogy is equivalent to solving a jigsaw puzzle where we do not know what the solved puzzle will look like and there may still be some pieces missing. The process described in this chapter may in fact be applied to this problem if we first generate a hypothetical piece pattern and use it with all the known pieces patterns and create possible solution to the puzzle generate a meta-pattern hypothesis . If there are abundant instances that indicate prevalence of our meta-pattern hypothesis in the database we can associate a confidence and support to our discovery. Moreover in this 59 Learning Information Patterns .
