Data Mining: Multimedia, Soft Computing, and Bioinformatics
|
From inside the book
Results 1-3 of 23
Page 69
... Confidence : The confidence of the rules is defined by a confidence factor cf. We use [ 87 ] where wji cfj = inf j : all nodes in the path ( Σ¿Wji — 0j ) Σ ; ω ;; - ( 2.36 ) is the ith incoming link weight to node j and 0 ; is its ...
... Confidence : The confidence of the rules is defined by a confidence factor cf. We use [ 87 ] where wji cfj = inf j : all nodes in the path ( Σ¿Wji — 0j ) Σ ; ω ;; - ( 2.36 ) is the ith incoming link weight to node j and 0 ; is its ...
Page 270
... confidence ; ( ii ) 31 with 50 % support and 100 % confidence . The objective is to generate confident rules , having at least the minimum confidence . The problem decomposition proceeds as follows : • Find all sets of items that have ...
... confidence ; ( ii ) 31 with 50 % support and 100 % confidence . The objective is to generate confident rules , having at least the minimum confidence . The problem decomposition proceeds as follows : • Find all sets of items that have ...
Page 271
... Confidence = Support { 2,5 } = 100 % and Support { 2 } 52 with Confidence = Support { 2,5 } Support { 5 } = 100 % . However , in this method , multiple passes have to be made over the database for each different value of minimum support ...
... Confidence = Support { 2,5 } = 100 % and Support { 2 } 52 with Confidence = Support { 2,5 } Support { 5 } = 100 % . However , in this method , multiple passes have to be made over the database for each different value of minimum support ...
Contents
Soft Computing | 37 |
Multimedia Data Compression | 89 |
standard | 129 |
Copyright | |
9 other sections not shown
Other editions - View all
Data Mining: Multimedia, Soft Computing, and Bioinformatics Sushmita Mitra,Tinku Acharya Limited preview - 2005 |
Data Mining: Multimedia, Soft Computing, and Bioinformatics Sushmita Mitra,Tinku Acharya No preview available - 2005 |
Common terms and phrases
applications approach association rules ATATA binary Bioinformatics Boyer-Moore algorithm C₁ categorical classification clustering coding coefficients color components computational complexity content-based image retrieval corresponding data compression data mining database dataset decision tree decoder defined dictionary distance document domain efficient encoder outputs entropy entropy encoding evaluation example extracted feature fuzzy sets gene Hence Huffman code IEEE Transactions image retrieval initial input integer involving JPEG Karp-Rabin knowledge discovery knowledge-based network learning length linguistic matching algorithms matrix measure method mismatch Mitra multimedia data neural networks neuro-fuzzy neurons objects occurrence optimal partition pattern matching pixel prediction prefix protein pruning quantization query represented result rough set sample Section sequence shown in Fig soft computing split statistical string matching structure substring suffix symbol Table techniques text mining transformed vector wavelet Web mining weights