Data mining Notes Verified

Conclusions • We present a new indexing scheme for the general purposes of similarity search on Earth Mover's Distance • Our index method relies on the primal-dual theory to construct mapping functions from the origi ...

Conclusions • We present a new indexing scheme for the general purposes of similarity search on Earth Mover's Distance • Our index method relies on the primal-dual theory to construct mapping functions from the original probabilistic space to one-dimensional domain • Our B+ tree-based index framework has –High scalability –High efficiency –can handle High dimensional data Outline • Sources of HDD • Challenges of HDD • Searching and Mining Mixed Typed Data –Similarity Function on k-n-match –ItCompress • Bregman Divergence: Towards Similarity Search on Non-metric Distance • Earth Mover Distance: Similarity Search on Probabilistic Data • Finding Patterns in High Dimensional Data Sample/Row Enumeration Algorihtms • To avoid searching the large column/item enumeration space, our mining algorithm search for patterms/rules in the sample/row enumeration space • Our algorithms does not fitted into the column/item enumeration algorithms • They are not YAARMA (Yet Another Association Rules Mining Algorithm) • Column/item enumeration algorithms simply does not scale for microarray datasets Tree Search based Algorithms • Ullmann’s Algorithm (DFS) • A refinement procedure based on matrix of possible future matched vertex pairs to prune unfruitful matches • The simple enumeration algorithm for the isomorphisms between a graph G1 and a subgraph of another graph G2 with the adjacency matrices A1and A2 • An M’ matrix with |V1| rows and |V2 | columns can be used to permute the rows and columns of A2 to produce a further matrix P. If , then M’ specifies an isomorphism between G1 and the subgraph of G2. (a1 i , j  1)  ( pi, j  1) P  M '(M ' A )T 2