graph based cluster labeling using GHSOM
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This document proposes a method for graph-based cluster labeling using Growing Hierarchical Self-Organizing Maps (SOM). It represents documents as graphs to capture relationships between phrases. A Growing Hierarchical SOM is used for document clustering, where nodes represent document graphs. Keywords are extracted from each cluster based on term frequency, location, and number of matching phrases between document graphs. The method aims to provide descriptive labels for clusters as document collections increase in size.
graph based cluster labeling using GHSOM
- 1. Graph-based cluster labeling using Growing Hierarchal SOM Mahmoud Rafeek Alfarra College Of Science & Technology [email protected] The second International conference of Applied Science & natural Ayman Shehda Ghabayen College Of Science & Technology [email protected] Prepared by:
- 2. Out Line Labeling, What and why ? Graph based Representation 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: 1. The document collections grow larger. 2. Help To: work with processing of news, email threads, blogs, reviews, and search results
- 5. Labeling, What and why ? Documents collection Document Labeled Clusters Preprocessing Step DIG Model X B S OL A G C D Clustering Process + Labeling 0 G0 0 G1 0 Gs SOM 1 G0 1 G1 1 Gs 2 G1 2 G2 Hierarchal Growing SOM 2 G1 2 G2 1 G0 1 G1 2 G1 2 G2
- 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. e1 e0 e2 rafting adventures river Document Table e0 S1(1), S2(2), S3(1) e0 S2(1) e2 S1(2) e1 S4(1) fishing Doc TF ET 1 {0,0,3} 2 {0,0,2} 3 {0,0,1} S1(2( #Sentence Position of term
- 8. Graph based Representation Example Document 1 River rafting Mild river rafting River rafting trips Document 2 Wild river adventures River rafting vocation plan fishing trips fishing vocation plan booking fishing trips river fishing mild river rafting trips mild river rafting trips wild adventures vocation plan wild plan mild river rafting trips adventures vocation booking fishing +
- 10. Growing Hierarchal SOM Determining the winning node … v1 v2 v3 v5 v4 v7 e0 v6 e0 e1 e5 e3 e2 e4 n-nodes in SOM (Gs) v1 v2 v5 v7 e0 v6 e0 e1 e5 e3 Input Document Graph (Gi) Phrases Significance Gi Gs length Gi
- 11. Growing Hierarchal SOM Neuron updating in the graph domain A B D C e0 Xe0 e1 e5 e3 Y B D C Ee4 e1 e5 e3 A e2 e2 G1 G2 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: Term TF- Locations {T, L,B,b} No of matching phrases (MP) Weight Weight = (f1*T + f2*L + f3*B+ f4*b) * 0.4 + MP * 0.6
- 14. Extracting labeling of clusters T1 T2 T3 T10 T4 T7 T8 T11 T6 T5 T9 Term F-weight # MP Net weight T2 12.4 2 (T2,T3), (T2,T5) 4.96 + 1.2 =6.16 T3 10.2 2 (T2,T3), (T5,T3) 4.08 + 1.2= 5.28 T5 16.6 3 (T2,T5), (T8, T5), (T5,T3) 6.4+ 1.8= 6.4 T8 14.4 1 (T8,T5) 5.76+ 0.6=6.36
- 15. Thank You … Questions