Name
Abstract | ||
|---|---|---|
Cluster analysis is frequently used to study the trend of gene expression behaviours from microarray time series data. We adopt a partitioning-based clustering algorithm for such a task. After time series are discritised into sequences, a sequential pattern mining technique is applied to find patterns as the initial clusters. Longest Common Subseries Similarity is used to measure the similarity between time series which overcomes the 'shift-effect' influence. An object is re-assigned to the cluster which has most objects within the k nearest neighbours of the object. Similarity measurements, like Pearson correlation coefficient, are used to determine the neighbours. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1504/IJBIDM.2010.030299 | IJBIDM |
Keywords | Field | DocType |
longest common subseries similarity,time series,partitioning-based clustering algorithm,similarity measurement,microarray time series data,sequential pattern mining technique,pearson correlation coefficient,time series gene expression,gene expression behaviour,initial cluster,cluster analysis,microarrays,business intelligence,data mining,gene expression,sequential pattern mining | Data mining,Similitude,Cluster (physics),Correlation coefficient,Time series,Pearson product-moment correlation coefficient,Computer science,Cluster analysis,Sequential Pattern Mining,DNA microarray | Journal |
Volume | Issue | Citations |
5 | 1 | 4 |
PageRank | References | Authors |
0.43 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huang-Cheng Kuo | 1 | 42 | 23.87 |
| Tsung-Lung Lee | 2 | 4 | 0.43 |
| Jen-Peng Huang | 3 | 57 | 6.45 |