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Graph-based cluster labeling using Growing Hierarchal SOM

The second International conference of Applied Science & natural. Graph-based cluster labeling using Growing Hierarchal SOM. Prepared by:. Ayman Shehda Ghabayen College Of Science & Technology a.ghabayen@cst.ps. Mahmoud Rafeek Alfarra College Of Science & Technology m.farra@cst.ps.

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Graph-based cluster labeling using Growing Hierarchal SOM

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  1. The second International conference of Applied Science & natural Graph-based cluster labeling using Growing Hierarchal SOM Prepared by: AymanShehdaGhabayen College Of Science & Technology a.ghabayen@cst.ps Mahmoud Rafeek Alfarra College Of Science & Technology m.farra@cst.ps

  2. OutLine • Labeling, What and why ? • Graph basedRepresentation • Growing Hierarchal SOM • Extraction of labeles of clusters

  3. Labeling, What and why ? • Cluster labeling: process tries to select descriptive labels (Key words) for the clusters obtained through a clustering algorithm.

  4. Labeling, What and why ? • Cluster labeling is an increasingly important task that: • The document collections grow larger. • Help To: work with processing of news, email threads, blogs, reviews, and search results

  5. D C X B A 0G0 0G1 0Gs O L S SOM G 1G0 1G1 1G0 1Gs 1G1 Document Labeled Clusters 2G1 2G2 2G1 2G2 2G1 2G2 Hierarchal Growing SOM Labeling, What and why ? DIG Model Preprocessing Step Documents collection Clustering Process + Labeling

  6. Graph based Representation 1 0 0 0 0 0 X 0 1 0 1 1 0 A 2 5 9 6 3 7 N C B D S ph5 ph1 ph3 ph2 ph4 3,3 1,1 2,3 1,3

  7. Graph based Representation • Capture the silent features of the data. • DIG Model: a directed graph. • A document is represented as a vector of sentences • Phrase indexing information is stored in the graph nodes themselves in the form of document tables. fishing S1(2) e1 Position of term rafting river # Sentence e0 Document Table e0 S1(1), S2(2), S3(1) e2 e0 S2(1) e2 S1(2) e1 S4(1) adventures

  8. mild trips river rafting river trips plan rafting wild booking fishing mild river trips plan vocation adventures rafting wild vocation adventures Graph based Representation Document 1 Example + River rafting Mild river rafting River rafting trips mild Document 2 Wild river adventures River rafting vocation plan fishing trips fishing vocation plan booking fishing trips river fishing

  9. Growing Hierarchal SOM

  10. length Phrases Significance Gi Gi Gs e0 e0 Growing Hierarchal SOM • Determining the winning node e0 v1 v6 e0 v1 v6 v7 e3 v7 e3 e1 e5 v2 v5 e1 e5 v2 v5 e4 v3 e2 … v4 n-nodes in SOM (Gs) Input Document Graph (Gi)

  11. e0 Growing Hierarchal SOM • Neuron updating in the graph domain e0 G1 A X e2 e4 Y E G2 e2 A C C e3 e3 e1 e5 B D e1 e5 B D We choose increasing the matching phrases to update graphs due to its affect is more stronger than increasing terms (nodes) also add matching phrases can consider it as add ordered pair of nodes

  12. Over all Document clustering Process

  13. Extracting labeling of clusters • To extract the Key word, we need to build a table for each cluster as the following: Weight = (f1*T + f2*L + f3*B+ f4*b) * 0.4 + MP * 0.6

  14. Extracting labeling of clusters T3 T1 T4 T2 T5 T6 T7 T8 T11 T10 T9

  15. Thank You … Questions

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