Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/59878
Title: GPU-parallelized artificial bee colony algorithm (GP-ABC) in data clustering application
Authors: Anggacipta, Gerry
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
Issue Date: 2014
Abstract: Artificial Bee Colony (ABC) optimization and k-means algorithm are popularly used in data clustering application due to their accuracy and simplicity. However, as the number of dimension and data increases, program complexity may increase much further and ABC will execute in much slower time. This project proposes a novel parallelization model on ABC called ‘GPU-parallelized Artificial Bee Colony (GP-ABC)’ algorithm in order to achieve speedup relatively to its normal sequential program execution. Testing has been done on several datasets from UCI Machine Learning repository such as Iris and Wine datasets. The results were encouraging and outperformed the ordinary ABC algorithm in terms of processing time.
URI: http://hdl.handle.net/10356/59878
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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