A Stage-by-Stage Pruning Method for Classifying Uncertain Data Streams

Background: We study an important problem of similarity grouping processing on stream data that inherently contain uncertainty. Method: In this paper SBSP - [Stage by Stage Pruning] a novel pruning method is proposed for fast, accurate clustering and classifying the data where the two stages were grouped into a single framework MYFRAME. Findings : The proposed approach group the data-by-data level pruning using Manhattan distance in first stage. In the second stage, the data is grouped by object level pruning in hyperspace. Improvements: Currently, this approach is applied in real time applications such as object detection, video retrieval, people detection and tracking, earth quake monitoring etc.