Abstract
Pattern mining plays an essential role in many data mining tasks that attempt to find valuable patterns in massive databases. These patterns can include associations, correlations, sequences, episodes, classifiers and/or clusters. However, discovering such patterns is time-consuming because the extracted patterns are often too numerous and thus difficult to analyze by end users, especially when complex data structures are taken into consideration. Furthermore, existing work mainly. Concentrates on discovering common knowledge in a single data set; and not much attention has been given to identifying significant differences among several data sets. This thesis mainly targets four pattern mining topics on complex data structures to provide solutions to the above problems.
Keywords
Pattern mining
Association Rules
Probability-Based Frequent Subtree
BFS-based Candidate Subtree Generation
Authors
How to Cite this Article
S.Ramya, S.Gandhimathi, Dr V.Baby Deepa (2019).
"ANALYSIS OF PROBABILITY-BASED FREQUENT SUBTREE PATTERN MINING ALGORITHM IN COMPLEX DATA".
International Journal of Contemporary Research in Computer Science and Technology,
5(3), pp. 34-39.
