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Bi-Clustering

Bi-Clustering. Data Mining: Clustering. K-means clustering minimizes. Where. Clustering by Pattern Similarity ( p- Clustering). The micro-array “raw” data shows 3 genes and their values in a multi-dimensional space Parallel Coordinates Plots Difficult to find their patterns

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Bi-Clustering

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  1. Bi-Clustering

  2. Data Mining: Clustering K-means clustering minimizes Where

  3. Clustering by Pattern Similarity (p-Clustering) • The micro-array “raw” data shows 3 genes and their values in a multi-dimensional space • Parallel Coordinates Plots • Difficult to find their patterns • “non-traditional” clustering

  4. Clusters Are Clear After Projection

  5. Motivation • E-Commerce: collaborative filtering

  6. Motivation

  7. Motivation

  8. Motivation

  9. Motivation • DNA microarray analysis

  10. Motivation

  11. Motivation • Strong coherence exhibits by the selected objects on the selected attributes. • They are not necessarily close to each other but rather bear a constant shift. • Object/attribute bias • bi-cluster

  12. Challenges • The set of objects and the set of attributes are usually unknown. • Different objects/attributes may possess different biases and such biases • may be local to the set of selected objects/attributes • are usually unknown in advance • May have many unspecified entries

  13. Previous Work • Subspace clustering • Identifying a set of objects and a set of attributes such that the set of objects are physically close to each other on the subspace formed by the set of attributes. • Collaborative filtering: Pearson R • Only considers global offset of each object/attribute.

  14. bi-cluster • Consists of a (sub)set of objects and a (sub)set of attributes • Corresponds to a submatrix • Occupancy threshold • Each object/attribute has to be filled by a certain percentage. • Volume: number of specified entries in the submatrix • Base: average value of each object/attribute (in the bi-cluster)

  15. bi-cluster

  16. bi-cluster • Perfect -cluster • Imperfect -cluster • Residue: dij diJ dIJ dIj

  17. bi-cluster • The smaller the average residue, the stronger the coherence. • Objective: identify -clusters with residue smaller than a given threshold

  18. Cheng-Church Algorithm • Find one bi-cluster. • Replace the data in the first bi-cluster with random data • Find the second bi-cluster, and go on. • The quality of the bi-cluster degrades (smaller volume, higher residue) due to the insertion of random data.

  19. The FLOC algorithm Generating initial clusters Determine the best action for each row and each column Perform the best action of each row and column sequentially Y Improved? N

  20. The FLOC algorithm • Action: the change of membership of a row(or column) with respect to a cluster column M=4 1 2 3 4 row 3 4 2 2 1 M+N actions are Performed at each iteration 2 1 3 2 3 N=3 3 4 2 0 4

  21. The FLOC algorithm • Gainof an action: the residue reduction incurred by performing the action • Order of action: • Fixed order • Random order • Weighted random order • Complexity: O((M+N)MNkp) 

  22. The FLOC algorithm • Additional features • Maximum allowed overlap among clusters • Minimum coverage of clusters • Minimum volume of each cluster • Can be enforced by “temporarily blocking” certain action during the mining process if such action would violate some constraint.

  23. Performance • Microarray data: 2884 genes, 17 conditions • 100 bi-clusters with smallest residue were returned. • Average residue = 10.34 • The average residue of clusters found via the state of the art method in computational biology field is 12.54 • The average volume is 25% bigger • The response time is an order of magnitude faster

  24. Conclusion Remark • The model of bi-cluster is proposed to capture coherent objects with incomplete data set. • base • residue • Many additional features can be accommodated (nearly for free).

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