Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 8, Pages: 1-9
S. Subashini1 * and S. Appavu alias Balamurugan2
1Department of Computer Science Engineering, Fatima Michael College of Engineering and Technology, Madurai – 625020, Tamil Nadu, India; [email protected] 2Department of Information Technology, K.L.N. College of Information and Technology, Madurai – 630612, Tamil Nadu, India; [email protected]
*Author for Correspondence
S. Subashini Department of Computer Science Engineering, Fatima Michael College of Engineering and Technology, Madurai – 625020, Tamil Nadu, India; [email protected]
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.
Keywords: Clustering, Data Pruning, Distance, Group Nearest Neighbor, Grouping Process, Similarity Search, Uncertain Data Streams
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