####Difference between supervised and unsupervised:

• supervised:numerical
  • evaluation: accuracy, precision, recall
• unsupervised:
  • evaluation: how? - clusters are in the eye of beholder :-)

Cluster Analysis: Finding groups of objects such that the objects in a group will be similar to one another and different from the objects in other groups.

Measures of cluster validity:()

• external index:
  • entrop
  • y
• internal index:
  • Sum of Squared Error(SSE)
• relative index: used to compare two clusterings
  • SSE or entropy
  • cohesion and separation

The validation of clustering structures is the most difficult and frustrating part of cluster analysis

• Conductance: [reference][1]
• Coverage: [reference][2]
• Modularity: [reference][3]
• Performance: [reference][4]
• Silhouette index : reference [1]: # [2]: # [3]: # [4]: #

• internal measures: cohesion and separation
  
• external measures: entropy and purity
  

• comparing different algorithms
• Good roadmap, Completed Paper
• SOM (Self-organizing Map)

• cluster validation: reference --> idea: using binary search
• cluster validation(statistical approach) : reference
• for proofs: reference --> chapter 3
• solve two weaknesses of spectral clustering: reference --> method instead of k-means
• diffusion maps, spectral clustering and eigenfunctions of fokker-planck operators: reference --> euclidean distance in new representation has a meaningful description
• consistency of spectral clustering: annals of statistics 2008
• limits of spectral clustering: reference
• random walk survey: reference
• kdd bipartite spectral: reference
• ........
• co-training spectral: ICML2011
• mining clustering dimensions: ICML2010
• Large-Scale Multi-View Spectral Clustering via Bipartite Graph : AAAI2015
• Incremental Spectral Clustering with the Normalised Laplacian: NIPS2011

• good review: this
• validation methods: this
• kernel and spectral: this
• Spectral Clustering with Purturbed Data: nips 2009
• Spectral graph theory: chung 97
• ...

• dbscan visualization : reference
• dbscan code: code
• MDS(MultiDimensional Scaling): code
• numpy: tutorial

• R package: this

• stanford dataset: this
• list of repositories: reference
• wine: link
• Dermatology: link
• Letter Recognition: link
• handWritten digits: link
• UCI:
  • fisher iris
  • wine
  • breast cancer winsconsin
  • heart
  • handwritten digit

• Solving Cluster Ensemble Problems by Bipartite Graph Partitioning: link

• local clustering on multiple manifold: link
  • Train M d-dimensional local linear manifolds by using MPPCA to approximate the underlying manifolds
  • Determine the local tangent space of each point ( using EM )
  • Compute pairwise affinity between two local tangent spaces using
  • p_ij = (max dot(u_1, v_1) )x ... x(max dot(u_k,v_k) ) --> u_i in tangent space
  • q_ij = if in KNN 1, 0 o.w
  • w_ij = p_ij q_ij
• based on random walk: link
• based on neighbor propagation: link
  • construct similarity graph and neighborhood graph, propagate it
• based on newton equations: link
  • sparsify the affinity matrix using second newton equation
• Local density adaptive: link
• density sensitive: link

• Laplacian Score: nips 2005
• Spectral Feature Selection:2007
• Efficient Spectral Feature Selection with minimum dependency: 2010
• Semi-supervised Feature Selection via Spectral Analysis: 2007

• introduction to cluster analysis: reference
• Laplacian properties: reference, - , reference

• SCORE
• On Spectral Clustering
• Learnng Spectral Clustering
• Multiple non-redundant spectral clustering views

• for Kernel: Bernhard Scholkopf
• good papers: University of Washington
• regularized spectral clustering:jmlr
• model based clustering(select number of clusters): University of Mishigan
• statistical view of marginal spectral clustering: Jordan
• spectral dimension reduction: Jordan 2011