We present a powerful meta-clustering technique called Iterative Dou- ble Clustering (IDC). The IDC method is a natural extension of the recent Double Clustering (DC) method of Slonim and Tishby that ex- hibited impressive performance on text categorization tasks [12]. Us- ing synthetically generated data we empirically flnd that whenever the DC procedure is successful in recovering some of the structure hidden in the data, the extended IDC procedure can incrementally compute a signiflcantly more accurate classiflcation. IDC is especially advan- tageous when the data exhibits high attribute noise. Our simulation results also show the efiectiveness of IDC in text categorization prob- lems.

We present a powerful meta-clustering technique called Iterative Double Clustering (IDC). The IDC method is a natural extension of the recent Double Clustering (DC) method of Slonim and Tishby that exhibited impressive performance on text categorization tasks [12]. Using synthetically generated data we empirically find that whenever the DC procedure is successful in recovering some of the structure hidden in the data, the extended IDC procedure can incrementally compute a significantly more accurate classification. IDC is especially advantageous when the data exhibits high attribute noise. Our simulation results also show the effectiveness of IDC in text categorization problems. Surprisingly, this unsupervised procedure can be competitive with a (supervised) SVM trained with a small training set. Finally, we propose a simple and natural extension of IDC for semi-supervised and transductive learning where we are given both labeled and unlabeled examples.

We present a powerful meta-clustering technique called Iterative Double Clustering (IDC). The IDC method is a natural extension of the recent Double Clustering (DC) method of Slonim and Tishby that exhibited impressive performance on text categorization tasks [12]. Using synthetically generated data we empirically find that whenever the DC procedure is successful in recovering some of the structure hidden in the data, the extended IDC procedure can incrementally compute a significantly more accurate classification. IDC is especially advantageous when the data exhibits high attribute noise. Our simulation results also show the effectiveness of IDC in text categorization problems. Surprisingly, this unsupervised procedure can be competitive with a (supervised) SVM trained with a small training set. Finally, we propose a simple and natural extension of IDC for semi-supervised and transductive learning where we are given both labeled and unlabeled examples.

We present a powerful meta-clustering technique called Iterative Double Clustering(IDC). The IDC method is a natural extension of the recent Double Clustering (DC) method of Slonim and Tishby that exhibited impressiveperformance on text categorization tasks [12]. Using synthetically generated data we empirically find that whenever the DC procedure is successful in recovering some of the structure hidden in the data, the extended IDC procedure can incrementally compute a significantly more accurate classification. IDC is especially advantageous whenthe data exhibits high attribute noise. Our simulation results also show the effectiveness of IDC in text categorization problems. Surprisingly,this unsupervised procedure can be competitive with a (supervised) SVM trained with a small training set. Finally, we propose a simple and natural extension of IDC for semi-supervised and transductive learning where we are given both labeled and unlabeled examples.