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Knowledge in B.Sc(Computer Science)
Characteristics
Characteristics of data mining process ... Five processes i.e state the product, collect the data, perform preprocessing, estimate the model and interpret the model and draw conclusions
Ishant Goel
Classifications
Classifications of data mining process (based on kind of databases , knowledge, techniques and applications).... Then there are major issues and KDD
Data warehouse
Introduction of data warehouse( subject-oriented, integrated, time variant and non-volatile) and Data warehouse design process
Three-tier approach
This is a three tier data warehouse architecture... Explanation of Tier-1, Tier-2 and Tier-3... Data warehouse models and at last, meta data repository
Online analytical processing
Complete notes of Online analytical processing... It's three basic analytical operations(consolidation, drill-down and slicing-dicing)... Types( Relational, Multidimensional and hybrid)
Data preprocessing
Notes of data preprocessing includes data integration with diagram, issues in data integration, data transformation and data reduction
Rule mining
Association rule mining with a problem and example, important concept of rule mining and market based analysis with an example
Frequent pattern
Frequent pattern mining( based on completeness of pattern, based on levels of abstraction, based on number of data dimensions, based on types of values, based on kind of rules and based kn kinds of pattern)
Itemset mining
Efficient frequent itemset mining methods using candidate generation i.e apriori algorithm( coding, example and steps) Generation associating rules from frequent itemsets
Multilevel rules
Mining multilevel association rules Here is a concept hierarchy for AllElectronics computer items... Approaches for rules (uniform minimum support and reduced minimum support and group-based minimum support)
Relational Databases
Mining Multidimensional association rules from relational databases and mining quantitative association rules and in last, from association mining to correlation analysis
Classification and prediction
This is the introduction of classification and prediction,issues regarding them i.e. data cleaning, relevance analysis, data transformation and reduction and in last, comparing classification and prediction methods