Name
Abstract | ||
|---|---|---|
AbstractAssociation rules mining and classification rules discovery are two important data mining techniques used to expose the relations among large sets of data items. The technique aims to find out the rules that satisfy the predefined minimum support and the confidence. Association rules mining has successfully been implemented in biomedical research and has demonstrated encouraging results in analysing the gene expression data in order to discover the relevant biological association among different genes, gene expression, and various protein properties like protein functionality and sequence similarity. In this paper, we applied the association rule mining technique - the ACO-AC to the problem of classifying proteins into its correct fold of the SCOP dataset. The technique combines the association rules mining and supervised classification mechanism using ant colony optimisation. Experimental results reveal the classifier performance in protein classification problem as excellent by identifying most accurate and compact rules. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1504/IJBIC.2016.074631 | Periodicals |
Keywords | Field | DocType |
association rules mining, classification, rules discovery, structural classification of proteins, SCOP, ant colony optimisation, ACO | Data mining,Structural classification,Association rule learning,Artificial intelligence,Ant colony,Classifier (linguistics),Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
8 | 1 | 1758-0366 |
Citations | PageRank | References |
2 | 0.35 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muhammad Asif Khan | 1 | 4 | 2.80 |
| Waseem Shahzad | 2 | 70 | 8.91 |
| Abdul Rauf Baig | 3 | 126 | 15.82 |