Discoverig Genomic Patterns Using Fuzzy Self Organising Maps
| dc.contributor.author | Chun, Ho Yi | |
| dc.date.accessioned | 2018-08-01T07:37:14Z | |
| dc.date.available | 2018-08-01T07:37:14Z | |
| dc.date.issued | 2009-02 | |
| dc.description.abstract | Microarray technology has allowed for the collection of thousands of genetic data simultaneously. The problem with microarray data lies in the fact that while there are thousands of attributes, the number of samples is few. Clustering becomes a practical option in analysing such data as it is impractical to study every data individually. Although there are numerous clustering methods such as k-means and hierarchical clustering, it is insufficient when it comes to a massive scale dataset. Self-organising maps (SOM) is a concept introduced by Teuvo Kohonen that allowed for an unsupervised clustering and subsequent visualisation of data. This visualisation is by means of projecting the data onto a 2-dimensional map grid while retaining relations among map units that represent the data. Furthermore, the use of component planes in SOM allows for the analysis of individual attributes and correlation between attributes. On the whole, this clustering and visualisation allows for a “big picture” analysis of a massive dataset. In genetics, hard clustering may not be entirely suitable as multiple genes can have multiple traits, and thus, in clustering, can belong to multiple clusters. This brings up the subject of incorporating fuzzy rules, where one attribute is not restricted to have only one characteristic. Therefore, a feasible solution would be a hybridisation of fuzzy c-means and SOM. While fuzzy SOM (FSOM) has been explored, it requires many parameters prior to processing. This work proposes a modified FSOM that requires very few initial parameters, and was tested on a brain tumour dataset and a breast cancer dataset. The results showed that the modified FSOM performed better than the ordinary SOM, and the results of the cluster analysis matched a prior work. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/6109 | |
| dc.language.iso | en | en_US |
| dc.publisher | Universiti Sains Malaysia | en_US |
| dc.subject | Genomic Patterns | en_US |
| dc.subject | Fuzzy Self Organising Maps | en_US |
| dc.title | Discoverig Genomic Patterns Using Fuzzy Self Organising Maps | en_US |
| dc.type | Thesis | en_US |
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