Context-Aware Clustering K-means Initialization 1st round Final Phrases

Context-Aware Clustering K-means Initialization 1st round Final Phrases www.phwiki.com

Context-Aware Clustering K-means Initialization 1st round Final Phrases

Bee, Jim, Music Director has reference to this Academic Journal, PHwiki organized this Journal Context-Aware Clustering Junsong Yuan in addition to Ying Wu EECS Dept., Northwestern University Contextual pattern in addition to co-occurrences Spatial contexts provide useful cues as long as clustering K-means revisit Assumption: data samples are independent Binary label indicator Limitation: contextual in as long as mation of spatial dependency is not considered in clustering data samples EM Update

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Clustering higher-level patterns Regularized k-means Distortion in original feature space Distortion in hamming space charactering contextual patterns Same as traditional K-means clustering Regularization term due to contextual patterns Not a smooth term! Chicken in addition to Egg Problem Hamming distance in clustering contextual patterns Matrix as long as m Cannot minimize J1 in addition to J2 separately ! J1 is coupled with J2 Decoupling Fix Update Fix Update

Nested-EM solution Nested E-step M-step Update in addition to separately the nested-EM algorithm can converge in finite steps. Theorem of convergence Simulation results (feature space) Simulation results (spatial space)

K-means Initialization 1st round Final Phrases

Multiple-feature clustering Dataset: h in addition to written numerical (‘0’-‘9’) from UCI data set Each digit has three different types of features Contextual pattern corresponds to compositional feature Different types of features serve as contexts of each other Clustering each type of features into 10 “words” Clustering 10 “phrases” based on a word-lexicon of size 3×10 Conclusion A context-aware clustering as long as mulation proposed Targets on higher-level compositional patterns in terms of co-occurrences Discovered contextual patterns can feed back to improve the primitive feature clustering An efficient nested-EM solution which is guaranteed to converge in finite steps Successful applications in image pattern discovery in addition to multiple-feature clustering Can be applied to other general clustering problems

Bee, Jim KZUA-FM Music Director www.phwiki.com

Bee, Jim Music Director

Bee, Jim is from United States and they belong to KZUA-FM and they are from  Lakeside, United States got related to this Particular Journal. and Bee, Jim deal with the subjects like Music

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