Data Mining: Multimedia, Soft Computing, and Bioinformatics
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From inside the book
Page 18
... dataset of training records with several attributes . There is one distinguished attribute called the dependent attribute . The remaining predictor attributes can be numerical or categorical in nature . A numerical attribute has continu ...
... dataset of training records with several attributes . There is one distinguished attribute called the dependent attribute . The remaining predictor attributes can be numerical or categorical in nature . A numerical attribute has continu ...
Page 20
... dataset , say , a set of transactions . Market basket data consist of a set of items bought together by customers , one such set of items being called a transaction . A lot of work has been done in recent years to find associations ...
... dataset , say , a set of transactions . Market basket data consist of a set of items bought together by customers , one such set of items being called a transaction . A lot of work has been done in recent years to find associations ...
Page 21
... dataset to generate the rules . The objective is to predict a predefined class or goal attribute , which can never appear in the antecedent part of a rule . The generated rules are used to predict the class attribute of an unknown test ...
... dataset to generate the rules . The objective is to predict a predefined class or goal attribute , which can never appear in the antecedent part of a rule . The generated rules are used to predict the class attribute of an unknown test ...
Page 26
... datasets and high dimensionality . Huge datasets create combi- natorially explosive search space for model induction , and they increase the chances that a data mining algorithm will find spurious 26 26 INTRODUCTION TO DATA MINING.
... datasets and high dimensionality . Huge datasets create combi- natorially explosive search space for model induction , and they increase the chances that a data mining algorithm will find spurious 26 26 INTRODUCTION TO DATA MINING.
Page 27
... Datasets used for mining are usually huge and available from distributed sources . As a result , often the presence of spurious data points leads to over - fitting of the models . Regularization and re - sampling methodologies need to ...
... Datasets used for mining are usually huge and available from distributed sources . As a result , often the presence of spurious data points leads to over - fitting of the models . Regularization and re - sampling methodologies need to ...
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