Data Mining: Multimedia, Soft Computing, and Bioinformatics
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Page 20
... find associations among items in large groups of transactions [ 39 , 40 ] . A rule is normally expressed in the form X⇒ Y , where X and Y are sets of attributes of the dataset . This implies that transactions which contain X also ...
... find associations among items in large groups of transactions [ 39 , 40 ] . A rule is normally expressed in the form X⇒ Y , where X and Y are sets of attributes of the dataset . This implies that transactions which contain X also ...
Page 22
... find the occurrence ( s ) of the pattern P in T ( m ≤ n ) . = Several variants of the basic problem can be considered . The pattern may consist of a finite set of sequences P { P1 , P2 , ... , Pk } , where each Pi is a pattern from the ...
... find the occurrence ( s ) of the pattern P in T ( m ≤ n ) . = Several variants of the basic problem can be considered . The pattern may consist of a finite set of sequences P { P1 , P2 , ... , Pk } , where each Pi is a pattern from the ...
Page 25
... effectiveness of treatments , by analyzing patient disease history to find some relationship between diseases . • Molecular or pharmaceutical : Identify new drugs . • APPLICATIONS AND CHALLENGES 25 1.14 Applications and Challenges.
... effectiveness of treatments , by analyzing patient disease history to find some relationship between diseases . • Molecular or pharmaceutical : Identify new drugs . • APPLICATIONS AND CHALLENGES 25 1.14 Applications and Challenges.
Page 26
... Find affinity of visitors to Web pages , followed by subsequent layout modification . • Marketing : Help marketers discover distinct groups in their customer bases , and then use this knowledge to develop targeted marketing pro- grams ...
... Find affinity of visitors to Web pages , followed by subsequent layout modification . • Marketing : Help marketers discover distinct groups in their customer bases , and then use this knowledge to develop targeted marketing pro- grams ...
Page 27
... find spurious patterns that are not generally valid . Possible solutions include robust and efficient algorithms , sampling approximation methods , and parallel processing . Scaling up of existing techniques is needed for example , in ...
... find spurious patterns that are not generally valid . Possible solutions include robust and efficient algorithms , sampling approximation methods , and parallel processing . Scaling up of existing techniques is needed for example , in ...
Contents
1 | |
2 Soft Computing | 35 |
3 Multimedia Data Compression | 89 |
4 String Matching | 143 |
5 Classification in Data Mining | 181 |
6 Clustering in Data Mining | 227 |
7 Association Rules | 267 |
8 Rule Mining with Soft Computing | 293 |
9 Multimedia Data Mining | 319 |
An Application | 365 |
Index | 392 |
About the Authors | 399 |
Other editions - View all
Data Mining: Multimedia, Soft Computing, and Bioinformatics Sushmita Mitra,Tinku Acharya No preview available - 2005 |
Common terms and phrases
analysis applications association rules attributes binary Bioinformatics bits categorical character chromosome classification coding coefficients color components content-based image retrieval corresponding data compression data mining dataset decision tree decoder defined dictionary distance document domain encoded entropy entropy encoding evaluation example extracted feature frequent itemsets fuzzy sets genetic algorithms Hence Huffman code IEEE IEEE Transactions image retrieval initial input interaction involving JPEG knowledge discovery learning linguistic matrix measure method Mitra multimedia data neural networks neuro-fuzzy neurons node objects optimal output parameters partition pattern matching pixel prediction problem protein quantization query representation represented rough set S. K. Pal sample Section sequence shown in Fig soft computing spatial statistical string matching structure subbands subnetworks subsets substring symbol Table techniques text mining Transactions on Neural transformed vector visual wavelet Web mining weights