Semi-supervised clustering aims to improve clustering performance by considering user-provided si... more Semi-supervised clustering aims to improve clustering performance by considering user-provided side information in the form of pairwise constraints. We study the active learning problem of selecting must-link and cannot-link pairwise constraints for semi-supervised clustering. We consider active learning in an iterative framework; each iteration queries are selected based on the current clustering outcome and constraints available. We use the neighborhood framework where the pairwise points having the must-link belong to the same neigborhood and cannot-link pairwise points belong to the different neighborhood. If two points belong to the same neighborhood then they belong to the same cluster and viceversa. We will use the Glass Identification Data Set from the UCI machine learning repositories and investigate the improvement in clustering time using the Incremental Clustering.
Semi-supervised clustering aims to improve
clustering performance by considering user-provided si... more Semi-supervised clustering aims to improve clustering performance by considering user-provided side information in the form of pairwise constraints. We study the active learning problem of selecting must-link and cannot-link pairwise constraints for semi-supervised clustering. We consider active learning in an iterative framework; each iteration queries are selected based on the current clustering outcome and constraints available. We use the neighborhood framework where the pairwise points having the must-link belong to the same neigborhood and cannot-link pairwise points belong to the different neighborhood. If two points belong to the same neighborhood then they belong to the same cluster and viceversa. We will use the Glass Identification Data Set from the UCI machine learning repositories and investigate the improvement in clustering time using the Incremental Clustering.
Semi-supervised clustering aims to improve clustering performance by considering user-provided si... more Semi-supervised clustering aims to improve clustering performance by considering user-provided side information in the form of pairwise constraints. We study the active learning problem of selecting must-link and cannot-link pairwise constraints for semi-supervised clustering. We consider active learning in an iterative framework; each iteration queries are selected based on the current clustering outcome and constraints available. We use the neighborhood framework where the pairwise points having the must-link belong to the same neigborhood and cannot-link pairwise points belong to the different neighborhood. If two points belong to the same neighborhood then they belong to the same cluster and viceversa. We will use the Glass Identification Data Set from the UCI machine learning repositories and investigate the improvement in clustering time using the Incremental Clustering.
Semi-supervised clustering aims to improve clustering performance by considering user-provided si... more Semi-supervised clustering aims to improve clustering performance by considering user-provided side information in the form of pairwise constraints. We study the active learning problem of selecting must-link and cannot-link pairwise constraints for semi-supervised clustering. We consider active learning in an iterative framework; each iteration queries are selected based on the current clustering outcome and constraints available. We use the neighborhood framework where the pairwise points having the must-link belong to the same neigborhood and cannot-link pairwise points belong to the different neighborhood. If two points belong to the same neighborhood then they belong to the same cluster and viceversa. We will use the Glass Identification Data Set from the UCI machine learning repositories and investigate the improvement in clustering time using the Incremental Clustering.
Semi-supervised clustering aims to improve
clustering performance by considering user-provided si... more Semi-supervised clustering aims to improve clustering performance by considering user-provided side information in the form of pairwise constraints. We study the active learning problem of selecting must-link and cannot-link pairwise constraints for semi-supervised clustering. We consider active learning in an iterative framework; each iteration queries are selected based on the current clustering outcome and constraints available. We use the neighborhood framework where the pairwise points having the must-link belong to the same neigborhood and cannot-link pairwise points belong to the different neighborhood. If two points belong to the same neighborhood then they belong to the same cluster and viceversa. We will use the Glass Identification Data Set from the UCI machine learning repositories and investigate the improvement in clustering time using the Incremental Clustering.
Semi-supervised clustering aims to improve clustering performance by considering user-provided si... more Semi-supervised clustering aims to improve clustering performance by considering user-provided side information in the form of pairwise constraints. We study the active learning problem of selecting must-link and cannot-link pairwise constraints for semi-supervised clustering. We consider active learning in an iterative framework; each iteration queries are selected based on the current clustering outcome and constraints available. We use the neighborhood framework where the pairwise points having the must-link belong to the same neigborhood and cannot-link pairwise points belong to the different neighborhood. If two points belong to the same neighborhood then they belong to the same cluster and viceversa. We will use the Glass Identification Data Set from the UCI machine learning repositories and investigate the improvement in clustering time using the Incremental Clustering.
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Papers by Jamil Ahmed
clustering performance by considering user-provided side
information in the form of pairwise constraints. We study the
active learning problem of selecting must-link and cannot-link
pairwise constraints for semi-supervised clustering. We
consider active learning in an iterative framework; each
iteration queries are selected based on the current clustering
outcome and constraints available. We use the neighborhood
framework where the pairwise points having the must-link
belong to the same neigborhood and cannot-link pairwise
points belong to the different neighborhood. If two points
belong to the same neighborhood then they belong to the same
cluster and viceversa. We will use the Glass Identification
Data Set from the UCI machine learning repositories and
investigate the improvement in clustering time using the
Incremental Clustering.
clustering performance by considering user-provided side
information in the form of pairwise constraints. We study the
active learning problem of selecting must-link and cannot-link
pairwise constraints for semi-supervised clustering. We
consider active learning in an iterative framework; each
iteration queries are selected based on the current clustering
outcome and constraints available. We use the neighborhood
framework where the pairwise points having the must-link
belong to the same neigborhood and cannot-link pairwise
points belong to the different neighborhood. If two points
belong to the same neighborhood then they belong to the same
cluster and viceversa. We will use the Glass Identification
Data Set from the UCI machine learning repositories and
investigate the improvement in clustering time using the
Incremental Clustering.