Name
Abstract | ||
|---|---|---|
In presence of multiple clustering solutions for the same dataset, a clustering ensemble approach aims to yield a single clustering of the dataset by achieving a consensus among the input clustering solutions. The goal of this consensus is to improve the quality of clustering. It has been seen that there are some image clustering tasks that cannot be easily solved by computer. But if these images can be outsourced to the general people (crowd workers) to group them based on some similar features, and opinions are collected from them, then this task can be managed in an efficient manner and time effective way. In this work, the power of crowd has been used to annotate the images so that multiple clustering solutions can be obtained from them and thereafter a Markov chain based ensemble method is introduced to make a consensus of multiple clustering solutions. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Human-Computer Interaction | Data mining,Fuzzy clustering,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Computer science,Consensus clustering,Constrained clustering,Artificial intelligence,Cluster analysis,Machine learning,Single-linkage clustering |
DocType | Volume | Citations |
Journal | abs/1609.01484 | 1 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sujoy Chatterjee | 1 | 12 | 4.80 |
| Enakshi Kundu | 2 | 1 | 0.34 |
| Anirban Mukhopadhyay | 3 | 711 | 50.07 |