|
|
 |
| |
|
|
|
Eliminating Effect of Complement Cluster |
|
|
|
PP: 1-7 |
|
|
|
Author(s) |
|
|
|
Zahra Rezaei,,
|
|
|
|
Abstract |
|
|
| In this paper a new criterion for clusters validation is proposed. This new cluster validation criterion is used to approximate the goodness of a cluster. A clustering ensmble framework based on the new metric is proposed. The main idea behind the framework is to extract the most stable clusters in terms of the defined criteria. After extracting a large number of clusters some of them are selected for final ensmble. The clusters which satisfy a threshold of the proposed metric are selected to participate in final clustering ensemble. For combining the chosen clusters, a co-association based consensus function is applied. To combine a set of partitions into one consensus partition, hierarchical clustering algorithms can be employed where first the EAC method is applied over the output partitions to convert them into a co-association matrix and then considering it as a new data space bring a consensus partition out of them. But in proposed method due to having a set of clusters instead of a set of partitions, to extract the best representative consensus partition out of the set of chosen clusters the EAC method cannot be employed, and then we turn to a new EAC based method which is called Extended EAC, EEAC. EEAC is applied to construct the co-association matrix from the subset of clusters. Finally employing a simple hierarchical clustering algorithm as final consensus function the final representative partition is produced. Employing this new cluster validation criterion, the obtained ensemble is evaluated on some well-known and standard data sets. The empirical studies show promising results for the ensemble obtained using the proposed criterion comparing with the ensemble obtained using the standard clusters validation criterion |
|
|
|
|
|
|
|
