This progress report summarizes work on co-clustering methods for documents and words, including a new proposed method called CCAM (Co-clustering with Augmented Matrix). It introduces baseline K-means clustering, existing ITCC method, and the new CCAM method. Results are evaluated using both a results-based approach comparing cluster assignments, and a feature-based approach using cluster assignments as additional features. CCAM outperforms ITCC and baseline methods on average F-measure for ad and user clustering across different values of K clusters. Future work includes discretizing features and exploring different CCAM parameters.

1. Progress reports
2010/7/15
Student / Rui-Zhe Liu, Meng-Lun Wu
Advisor / Chia-Hui Chang

2. Outline
 Introduction
 Methods
 Baseline(K-means)
 ITCC (Information theoretic co-clustering)
 CCAM (Co-clustering with augmented matrix )
 Evaluations
 Results-based approach
 Feature-based approach
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3. Introduction(1/2)
 Dhillon et al. proposed information theoretic co-
clustering (ITCC) to progress two way clustering for the
document-word matrix.
 Sometimes we have addition information (called
augmented matrix) which are not considered by ITCC.
 For example, in addition to user-ad link matrix, we may have
user description matrix and advertisement description
matrix.
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4. Introduction (2/2)
 To fully utilize augmented matrix, we proposed a new
method called Co-clustering with augmented
matrix (CCAM).
 We also use the mutual information to model each data.
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5. Methods
User id Ad features
 Baseline(k-means):
 Data:
Ad_id Ad_id
 ad feature + ad-user link matrix
 lohas game + user-ad link matrix
 ITCC:
 Data: ad-user link matrix
 CCAM
 Data: ad-user link, ad feature, lohas game matrix
 Each method generates its ad clusters, and user group
results matrix.
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6. Evaluations(1/8)
 Each method evaluated by classification methods,
including SVM, decision tree, simple CART.
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7. Evaluations(2/8) – result based
 Method: baseline(k-means), co-clustering
 Evaluation data:
 ad feature + ad-user link + method results(ad cluster) matrix
 lohas game + user-ad link + method results(user group) matrix
 Results are as follows.
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8. Evaluations(3/8)
Evaluation of ad cluster (K=5)
1.2
1
0.8
F-measure
0.6
0.4
0.2
0
svm Cart
kart decision tree
co-clustering 0.312 0.277 0.349
baseline 0.965 0.826 0.822
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9. Evaluations(4/8)
Evaluation of user group (K=5)
1
0.9
0.8
0.7
F-measure
0.6
0.5
0.4
0.3
0.2
0.1
0
svm kart
Cart decision tree
co-clustering 0.861 0.729 0.729
baseline 0.931 0.677 0.677
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10. Evaluations(5/8)
 K-means is better unfortunately, because it generates the
standard answers for classifications.
 Therefore, we propose another way to evaluate.
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11. Evaluations(6/8) – feature based
 Method: baseline
 Evaluation data:
 ad feature + ad-user link data + baseline(k-means) results matrix
 lohas game + user-ad link data + baseline(k-means) results matrix
 Method: ITCC, CCAM
 Evaluation data:
 ad feature + ad-user link data + co-clustering feature (ad-user group
matrix) + baseline(k-means) results matrix
 lohas game + user-ad link data + co-clustering feature (user-ad cluster
matrix) + baseline(k-means) results matrix
 Results are as follows. User id methods User group
Ad_id Ad_id
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12. Evaluations(7/8)
Co-clustering comparing of ad clustering
1.050
1.000
Average F-measure
0.950
ccammethod
our
itcc
0.900
baseline
0.850
0.800
k=2 k=3 k=4 k=5
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13. Evaluations(8/8)
Co-clustering comparing of user group
1.000
0.950
Average F-measure
0.900
ccam
our method
0.850
itcc
0.800 baseline
0.750
0.700
k=2 k=3 k=4 k=5
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14. Future work
 Discretize ad feature data.
 Try different parameters for CCAM.
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15. Thank you for listening.