Enhanced E-tree for Mining High Dimensional Data

S. Salam*, M. Roja**, T. V. Rao***
* Associate Professor, Department of Computer Science and Engineering, Sree Vidyanikethan Engineering College, Tirupati, India.
** PG Scholar, Department of Computer Science and Engineering, Sree Vidyanikethan Engineering College, Tirupati, India.
*** Professor, Department of Computer Science and Engineering, Sree Vidyanikethan Engineering College, Tirupati, India.
Periodicity:November - January'2016

Abstract

Data Stream classification is one of the critical tasks in data mining. At the point when DataStream touches the base at a pace of GB/sec, we need to recognize spam, web observing and capacity. It is a troublesome operation and falls flat in the existing System. Actualizing two Algorithms namely, E-tree Algorithm (Ensemble-tree) and Avaricious Algorithm and Executing E-tree algorithm, the authors have maintained a strategic distance from the existing issues. Ensemble tree (Etree) takes care of extensive volumes of stream data and drifting. E-tree, Classifies and groups the Data Stream and stores the data effectively. Furthermore, foresee web checking and spam identification precisely. Controlling the web movement, the authors have actualized the greedy algorithm.

Keywords

E-Tree (Ensemble Tree), Data Stream, Web Monitoring.

How to Cite this Article?

Salam, S., Roja, M., and Rao, T. V. (2016). Enhanced E-tree for Mining High Dimensional Data. i-manager’s Journal on Cloud Computing, 3(1), 24-29.

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