Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Redefining Clustering for High-Dimensional Applications
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Transactions on rough sets XII
Frequent pattern trend analysis in social networks
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Integration analysis of diverse genomic data using multi-clustering results
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
Application of emerging patterns for multi-source bio-data classification and analysis
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Hi-index | 0.00 |
Using gene expression data for cancer detection is one of the famous research topics in bioinformatics. Theoretically, gene expression data is capable to detect all types of early cancer development in molecular level. Traditional clustering and pattern mining algorithm are either inadequate to handle high dimensional gene expression data effectively or the results obtained are not easy to understand. We proposed emerging pattern based projected clustering (EPPC) approaches to cope with the cancer detection problem. Previous result shows that easy understandable clusters are obtained. In this paper, the dimension projection process of EPPC is further studied and experimental results showed that the resulting clusters obtained by EPPC give comparable accuracy in classification when compared with ORCLUS.