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Tutorials
Chapter1: Inroduction to Data Mininig1.1 Introduction –Universe of Data
Information Technology has grown in various directions in the recent years. One natural evolutionary path has been the development of the database industry and its functionalities. Data collection, data creation, data management (including its storage and retrieval, database transaction processing) and data analysis and data understanding (involving data warehousing and data mining) has been the way in which it has progressed so far. Lot of information is produced in this world. So much information is sometimes produced by international organizations that are difficult to read them even through a life time. Information explosion has caused a problem in many fields from medicine, to manufacturing to market.........For more information download the following file.
ch1_introduction.pdf | |
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2.1 Classification and prediction
We treat many things as a group of things e.g., staff, students’ etc. In order to define a class ( a group of entities) a set of models that define and distinguish data classes or concept are delineated together. Using this class we get the ability to predict whether any new model belongs to this class or not i.e. a data model for whose class value is unknown can be predicted based on classification rules. There are various ways to apply rules e.g., classification (if-then) rules, decision trees, mathematical formula or neural networks. Classification can be used to predict the class label of the data object. However, classification is most useful in predicting certain missing values or unavailable data within a class. Normally, when classification is used to predict missing values in numeric data this is referred to as prediction. Data values prediction is more useful over class label assignment to an unknown object. This helps in making trend analysis based on available data.........For more information download the following file.
ch2_classifipredi.pdf | |
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Chatpter3:Data Warehouse
A data warehouse provides tools for executives and business managers to systematically organize, understand and use their data to make strategic decisions. It is a must have latest marketing weapon and a way to keep customers, by learning more about their needs.
Many possible definitions are there for a data warehouse. A data warehouse is a copy of transaction data specifically structured for query and analysis. Sometimes non-transaction data are stored in a data warehouse-through probably 95-99% of the data usually are transaction data . It is “query and analysis” because the main output from data warehouse systems are either tabular listings (queries) with minimal formatting or highly formatted”formal”reports.W.H.Inmon a leading architect in the construction of data warehouse systems defines it to be “A data ware house is a subject-oriented, integrated and time variant volatile data in collection of data in support of management’s decision making process”....for more information download the following files
ch3_dw_olap.pdf | |
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ch4_dw_architecture.pdf | |
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ch_5_data_preprocessing.pdf | |
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ch6_wavelet_transformation.pdf | |
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ch_7discretization_and_concept_hierarchy_generation.pdf | |
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ch_8data_mining_primitives.pdf | |
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ch_9data_mining_query_language.pdf | |
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ch_10concept_description.pdf | |
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ch_11dmql.pdf | |
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ch_12analytical_characterization.pdf | |
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ch_13mining_class_comparisons.pdf | |
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ch14_min_assoc_rules.pdf | |
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ch15multilevel_association_rules.pdf | |
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ch16classification_and_prediction.pdf | |
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ch_17decisiontree_induction.pdf | |
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ch_18classification_by_back_propagation.pdf | |
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ch_19_other_classification_methods.pdf | |
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ch_20cluster_analysis.pdf | |
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ch_21major_clustering_methods.pdf | |
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ch_22_density-based.pdf | |
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ch_23_mining_complex_types_of_data.pdf | |
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24_ch-mining_spatial_databases.pdf | |
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25_ch-mining_multimedia_databases.pdf | |
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26_ch-mining_text_databases.pdf | |
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27_ch_mining_the_world_wide_web.pdf | |
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28_ch-_applications_and_trends_in_data_mining.pdf | |
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29-____________data_mining_system_products_and_research_prototypes.pdf | |
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30-additional_themes_on_data_mining.pdf | |
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