IDT: MINING DATA STREAMS USING INDETERMINATE DECISION TREE ALGORITHM?

One-to-many data linkage is an important task in many domains, yet only a handful of prior publications have addressed this issue. The technical challenge in implementing one to many data linkage using one class clustering tree is it do not support uncertain data in the sense of real time incoming data and it support only for data mining but not for data stream. The system investigates the problem of implementing uncertain data conditions and supports the data linkage for the data stream. The tree is built using Indeterminant decision tree algorithm such that it is easy to understand and transform. There are two type of pruning method used for inducing the indeterminate decision tree algorithm. They are Basic pruning and end point pruning. The system gives more efficient result in terms of number of iteration when compared to one class clustering tree based data linkage.