1. 2015/12/251 Hierarchical Document Clustering Using Frequent Itemsets Benjamin C.M. Fung, Ke Wangy and Martin Ester Proceeding of International Conference on Data Mining, SIAM 2003 報告人 : 吳建良

2. Outline Hierarchical Document Clustering Proposed Approach Frequent Itemset-based Hierarchical Clustering (FIHC) Experimental Evaluation Conclusions 2

3. Hierarchical Document Clustering Document Clustering Automatically organize documents into clusters Documents within a cluster have high similarity Documents within different clusters are very dissimilar Hierarchical Document Clustering 3 Sports SoccerTennis Tennis ball

4. Challenges in Hierarchical Document Clustering High dimensionality. High volume of data Consistently high clustering quality. Meaningful cluster description 4

5. Overview of FIHC 5 Preprocessing Documents (High dimensional doc vectors) Generate frequent itemsets (Reduced dimensions feature vectors) Construct clustersBuild a TreePruning Cluster Tree

6. Preprocessing Remove stop words and Stemming Construct vector model doc i = ( item frequency 1, if 2, if 3, …, if m ) EX: 6

7. Generate Frequent Itemsets Use Agrawal et al. proposed algorithm to find global frequent itemsets Minimum global support a percentage of all documents Global frequent itemset a set of items (words) that appear together in more than a minimum global support of the whole document set Global frequent item an item that belongs to some global frequent itemset 7

8. Reduced Dimensions Vector Model High dimensional vector model Set the minimum global support to 35% Store the frequencies only for global frequent items 8

9. Construct Initial Clusters Construct a cluster for each global frequent itemset All documents containing this itemset are included in the same cluster 9 C(flow)C(form)C(layer)C(patient) cran.1 cran.2 cran.3 cran.4 cran.5 Its cluster label is {result} C(result)C(treatment)C(flow, layer) C(patient, treatment) cisi.1 cran.1 cran.3 med.2 med.5 cran.1 cran.2 cran.3 cran.4 cran.5 med.1 med.2 med.3 med.4 med.5 med.6 cran.3 med.1 med.2 med.4 med.6 med.1 med.2 med.3 med.4 med.6 cran.1 cran.2 cran.3 cran.4 cran.5 med.1 med.2 med.3 med.4 med.6

10. Cluster Frequent Items A global frequent item is cluster frequent in a cluster C i if the item is contained in some minimum fraction of documents in C i Suppose the minimum cluster support is set to 70% 10 C(patient) (flow, form, layer, patient, result, treatment) med.1=( 0 0 0 8 1 2 ) med.2=( 0 1 0 4 3 1 ) med.3=( 0 0 0 3 0 2 ) med.4=( 0 0 0 6 3 3 ) med.5=( 0 1 0 4 0 0 ) med.6=( 0 0 0 9 1 1 ) C(patient) ItemCluster Support form33% patient100% result66% treatment83%

11. 11 Initial Cluster (minimum cluster support=70%)

12. Cluster Label vs. Cluster Frequent Items Cluster label Use global frequent itemset as cluster label A set of mandatory items in the cluster Every document in the cluster must contain all the items in the cluster label Used in hierarchical structure establishment Cluster frequent items Appear in some minimum fraction of documents in the cluster Used in similarity measurement Topic description of the cluster 12

13. Make Clusters Disjoint Initial clusters are not disjoint Remove the overlapping of clusters Assign a document to the “best” initial cluster Define the score function Score(Ci ← doc j ) Measure the goodness of a cluster C i for a document doc j 13

14. Score Function Assign each doc j to the initial cluster C i that has the highest score 14 x represents a global frequent item in doc j and the item is also cluster frequent in C i x’ represents a global frequent item in doc j but the item is not cluster frequent in C i n(x) is the frequency of x in the feature vector of doc j n(x’) is the frequency of x’ in the feature vector of doc j

15. Score Function (cont.) If the highest score are more than one Choose the one that has the most number of items in the cluster label Key idea: A cluster C i is good for a document doc j if there are many global frequent items in doc j that appear in many documents in C i 15

16. Score Function - Example 16 C(flow) flow= 100% layer= 100% C(form) form= 100% C(layer) flow= 100% layer= 100% C(patient) patient= 100% treatment= 83% C(result) result= 100% patient= 80% treatment= 80% C(treatment) patient= 100% treatment= 100% result= 80% C(flow, layer) flow= 100% layer= 100% C(patient, treatment) patient= 100% treatment= 100% result= 80% (flow, form, layer, patient, result, treatment) med.6=( 0 0 0 9 1 1 ) 0+0-[(9 × 0.5)+(1 × 0.42)+(1 × 0.42)] = -5.34 -5.34 9.41 10.610.8-5.34 (9 × 1)+(1 × 1)+(1 × 0.8)= 10.8 global support of patient, result, and treatment

17. Recompute the Cluster Frequent Items 17 Recompute C i, also include all descendants of C i A descendant of C i if its cluster label is a superset of the cluster label of C i none (flow, form, layer, patient, result, treatment) med.5=( 0 1 0 4 0 0 ) Consider C(patient) C(patient). original ItemCluster Support form100% patient100% C(patient). descendant ItemCluster Support form33% patient100% result66% treatment83% Include descendant: C(patient, treatment) Disjoint cluster result

18. Building the Cluster Tree Put the more specific clusters at the bottom of the tree Put the more general clusters at the top of the tree 18

19. Building the Cluster Tree (cont.) Tree level Level 0: root, mark “null” and store unclustered documents Level k: cluster label is global frequent k-itemset Bottom-up manner Start from the cluster C i with the largest number k of items in its cluster label Identify all potential parents that are (k-1)-clusters and have the cluster label being a subset of C i ’s cluster label Choose the “best” among potential parents 19

20. Building the Cluster Tree (contd.) The criterion for selecting the best Similar to choosing the best cluster for a document Method: (1)Merge all the documents in the subtree of C i into a single conceptual document doc(C i ) (2)Compute the score of doc(C i ) against each potential parent C j The potential parent with the highest score would become the parent of C i All leaf clusters that contain no document can be removed 20

21. Example Start from 2-cluster C(flow, layer) and C(patient, treatment) C(flow, layer) is emptye  remove C(patient, treatment) Potential parents: C(patient) and C(treatment) C(treatment) is empty  remove C(patient) gets a higher score and becomes the parent of C(patient, treatment) 21 null C(flow) cran.1 cran.2 cran.3 cran.4 cran.5 C(form) cisi.1 C(patient) med.5 C(patient, treatment) med.1 med.2 med.3 med.4 med.6

22. Prune Cluster Tree A small minimum global support A cluster tree can be broad and deep Documents of the same topic are distributed over several small clusters Poor clustering accuracy The aim of tree pruning Produce a natural topic hierarchy for browsing Increase the clustering accuracy 22

23. Inter-Cluster Similarity Inter_Sim of C a and C b Reuse the score function to calculate Sim(C i ←C j ) 23

24. Property of Sim(C i ←C j ) Global support and cluster support are between 0 and 1 Maximum value of Score=, minimum is Normalize Score by, Sim is within -1~1 Avoid negative similarity value, add the term +1 The range of Sim function is 0~2, so is Inter_Sim Inter_Sim value is below 1 Weight of dissimilar items has exceeded the weight of similar items A good threshold to distinguish two clusters 24

25. Child Pruning Objective: shorten the depth of a tree Procedure 1. Scan the tree in the bottom-up order 2. For each non-leaf node, calculate Inter_Sim between the node and each of its children 3. If Inter_Sim is above 1, prune the child cluster 4. If a cluster is pruned, its children become the children of their grandparent Child pruning is only applicable to level 2 25

26. Example Determine whether cluster C(patient, treatment) should be pruned Compute the inter-cluster similarity between C(patient) and C(patient, treatment) Sim(C(patient)←C(patient, treatment)) Combine all the documents in cluster C(patient, treatment) by adding up their feature vectors 26 med.1=( 0 0 0 8 1 2 ) med.2=( 0 1 0 4 3 1 ) med.3=( 0 0 0 3 0 2 ) med.4=( 0 0 0 6 3 3 ) med.6=( 0 0 0 9 1 1 ) Sum = ( 0 1 0 30 8 9 ) (flow, form, layer, patient, result, treatment)

27. Example (cont.) Sim(C(patient, treatment)←C(patient))=1.92 Inter_Sim(C(patient)↔C(patient, treatment))= Inter_Sim is above 1, cluster C(patient, treatment) is pruned 27 null C(flow) cran.1 cran.2 cran.3 cran.4 cran.5 C(form) cisi.1 C(patient) med.1 med.2 med.3 med.4 med.5 med.6

28. Sibling Merging Sibling merging is applicable to level 1 Procedure 1. Calculate the Inter_Sim for each pair of clusters at level 1 2. Merge the cluster pair that has the highest Inter_Sim Repeat above steps until 1. User-specified number of clusters is reached, or 2. All cluster pairs at level 1 have Inter_Sim below or equal to 1 28 null C(flow) cran.1 cran.2 cran.3 cran.4 cran.5 cisi.1 C(patient) med.1 med.2 med.3 med.4 med.5 med.6

29. Experimental Evaluation Dataset Clustering Quality (F-measure) Recall, Precision Corresponding F-measure: F-measure for whole clustering result: 29 Cluster j Natural Class i n ij

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31. Efficiency & Scalability 31

32. Conclusions & Discussion This research exploits frequent itemsets for Define a cluster Use score function, construct initial clusters, make disjoint clusters Organize the cluster hierarchy Build cluster tree, prune cluster tree Discussion: Use unordered frequent word sets Different order of words may deliver different meaning Multiple topics of documents 32