Application of BTrees in data mining
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As massive amount of information are becoming available electronically, techniques for making the decision to analyze statistics on the large dataset are tending to be very complex. Making of such a decision requires more disk accesses in the main memory. So there is a need of such important techniques which can take least number of disk accesses as well as less running time to perform some operations in the main memory. Building of such a strategic goal oriented decision, there is requisite to classify the information into different classes with the help of some given properties of the information which enabled us to make two BTrees that are running simultaneous. One BTree is used as a classifier for making the decision and another bTree maintains the organization of the information of dataset from where we make the strategic decisions. Our research embodies around the learning, implementation and usage of advances data structure (i.e. BTree). In our thesis work we have used the binary search approach instead of the linear search takes running time O (T), has enhanced the performance of the BTree during execution of the operations on the BTree.
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