GPU-parallelized artificial bee colony algorithm (GP-ABC) in data clustering application
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 p...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2014
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| Online Access: | http://hdl.handle.net/10356/59878 |
| _version_ | 1826112245921218560 |
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| author | Anggacipta, Gerry |
| author2 | Kyle Rupnow |
| author_facet | Kyle Rupnow Anggacipta, Gerry |
| author_sort | Anggacipta, Gerry |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T03:03:50Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/59878 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:03:50Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | ntu-10356/598782023-03-03T20:29:55Z GPU-parallelized artificial bee colony algorithm (GP-ABC) in data clustering application Anggacipta, Gerry Kyle Rupnow School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition 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. Bachelor of Engineering (Computer Science) 2014-05-19T02:27:38Z 2014-05-19T02:27:38Z 2014 2014 Final Year Project (FYP) http://hdl.handle.net/10356/59878 en Nanyang Technological University 40 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition Anggacipta, Gerry GPU-parallelized artificial bee colony algorithm (GP-ABC) in data clustering application |
| title | GPU-parallelized artificial bee colony algorithm (GP-ABC) in data clustering application |
| title_full | GPU-parallelized artificial bee colony algorithm (GP-ABC) in data clustering application |
| title_fullStr | GPU-parallelized artificial bee colony algorithm (GP-ABC) in data clustering application |
| title_full_unstemmed | GPU-parallelized artificial bee colony algorithm (GP-ABC) in data clustering application |
| title_short | GPU-parallelized artificial bee colony algorithm (GP-ABC) in data clustering application |
| title_sort | gpu parallelized artificial bee colony algorithm gp abc in data clustering application |
| topic | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition |
| url | http://hdl.handle.net/10356/59878 |
| work_keys_str_mv | AT anggaciptagerry gpuparallelizedartificialbeecolonyalgorithmgpabcindataclusteringapplication |