Evaluation of FCV and FCM clustering algorithms in cluster-based compound selection
In the last few years, a number of available screening compounds has been growing rapidly due to the recent developments of high-throughput screening in drug discovery. Chemical vendors provide millions of compounds for drug lead identification; however, these compounds are highly redundant. Cluster...
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| Format: | Article |
| Language: | English |
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IEEE
2011
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| Online Access: | http://ir.unimas.my/id/eprint/16371/ http://ir.unimas.my/id/eprint/16371/1/Sinarwati.pdf |
| _version_ | 1848838050242625536 |
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| author | Sinarwati, Mohamad Suhaili Mohamad Nazim, Jambli |
| author_facet | Sinarwati, Mohamad Suhaili Mohamad Nazim, Jambli |
| author_sort | Sinarwati, Mohamad Suhaili |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | In the last few years, a number of available screening compounds has been growing rapidly due to the recent developments of high-throughput screening in drug discovery. Chemical vendors provide millions of compounds for drug lead identification; however, these compounds are highly redundant. Clustering method that groups similar compounds into families, can be used to analyze such redundancy. One of most used clustering method is cluster-based compound selection, which involves subdividing a set of compounds into clusters and choosing one compound or a small number of compounds from each cluster. However, little research has been done on overlapping method fuzzy c-means (FCM) and fuzzy c-varieties (FCV) clustering algorithms in compound selection research. Therefore, these two clustering algorithms are implemented and the performance is analyzed based on the effectiveness of the clustering results in terms of mean intercluster molecular dissimilarity (MIMDS) where these results are compared with one another. The analysis shows that in terms of MIMDS, the FCV is better than FCM because it clearly shown the uniform results compare to FCM clustering algorithm. |
| first_indexed | 2025-11-15T06:49:23Z |
| format | Article |
| id | unimas-16371 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:49:23Z |
| publishDate | 2011 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-163712021-07-05T11:58:29Z http://ir.unimas.my/id/eprint/16371/ Evaluation of FCV and FCM clustering algorithms in cluster-based compound selection Sinarwati, Mohamad Suhaili Mohamad Nazim, Jambli T Technology (General) In the last few years, a number of available screening compounds has been growing rapidly due to the recent developments of high-throughput screening in drug discovery. Chemical vendors provide millions of compounds for drug lead identification; however, these compounds are highly redundant. Clustering method that groups similar compounds into families, can be used to analyze such redundancy. One of most used clustering method is cluster-based compound selection, which involves subdividing a set of compounds into clusters and choosing one compound or a small number of compounds from each cluster. However, little research has been done on overlapping method fuzzy c-means (FCM) and fuzzy c-varieties (FCV) clustering algorithms in compound selection research. Therefore, these two clustering algorithms are implemented and the performance is analyzed based on the effectiveness of the clustering results in terms of mean intercluster molecular dissimilarity (MIMDS) where these results are compared with one another. The analysis shows that in terms of MIMDS, the FCV is better than FCM because it clearly shown the uniform results compare to FCM clustering algorithm. IEEE 2011 Article PeerReviewed text en http://ir.unimas.my/id/eprint/16371/1/Sinarwati.pdf Sinarwati, Mohamad Suhaili and Mohamad Nazim, Jambli (2011) Evaluation of FCV and FCM clustering algorithms in cluster-based compound selection. 7th International Conference on Information Technology in Asia (CITA 11), 2011. ISSN ISBN: 978-1-61284-130-4 http://ieeexplore.ieee.org/document/5999519/ DOI: 10.1109/CITA.2011.5999519 |
| spellingShingle | T Technology (General) Sinarwati, Mohamad Suhaili Mohamad Nazim, Jambli Evaluation of FCV and FCM clustering algorithms in cluster-based compound selection |
| title | Evaluation of FCV and FCM clustering algorithms in cluster-based compound selection |
| title_full | Evaluation of FCV and FCM clustering algorithms in cluster-based compound selection |
| title_fullStr | Evaluation of FCV and FCM clustering algorithms in cluster-based compound selection |
| title_full_unstemmed | Evaluation of FCV and FCM clustering algorithms in cluster-based compound selection |
| title_short | Evaluation of FCV and FCM clustering algorithms in cluster-based compound selection |
| title_sort | evaluation of fcv and fcm clustering algorithms in cluster-based compound selection |
| topic | T Technology (General) |
| url | http://ir.unimas.my/id/eprint/16371/ http://ir.unimas.my/id/eprint/16371/ http://ir.unimas.my/id/eprint/16371/ http://ir.unimas.my/id/eprint/16371/1/Sinarwati.pdf |