data mining concepts and techniques solution manual download
Data Mining Concepts and Techniques phần 1 potx
... Statistical Data Mining 66611.3.3 Visual and Audio Data Mining 66711.3.4 Data Mining and Collaborative Filtering 67011.4 Social Impacts of Data Mining 67511.4.1 Ubiquitous and Invisible Data Mining ... object-relational databases and specific application-oriented databases, such as spatial databases, time-series databases,text databases, and multimedia databases. The challenges and techniques of mining ... Reference Data in Enterprise Databases: Binding Corporate Data to the Wider WorldMalcolm Chisholm Data Mining: Concepts and Techniques Jiawei Han and Micheline KamberUnderstanding SQL and Java...
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Data Mining Concepts and Techniques phần 2 ppsx
... 972.7Summary Data preprocessing is an important issue for both data warehousing and data mining, as real-world data tend to be incomplete, noisy, and inconsistent. Data preprocessingincludes data cleaning, ... approximation of the original data. PCA is computationally inexpensive, can be applied to ordered and unorderedattributes, and can handle sparse data and skewed data. Multidimensional data of more than ... (inclusive).2.3 Data Cleaning 652.3.3 Data Cleaning as a ProcessMissing values, noise, and inconsistencies contribute to inaccurate data. So far, we havelooked at techniques for handling missing data and...
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Data Mining Concepts and Techniques phần 3 docx
... processing, and data mining. We also introduce on-line analytical mining (OLAM), a powerful paradigm thatintegrates OLAP with data mining technology.3.5.1 Data Warehouse Usage Data warehouses and data ... Warehouse and OLAP Technology: An Overview3.5From Data Warehousing to Data Mining “How do data warehousing and OLAP relate to data mining? ” In this section, we study theusage of data warehousing ... Chapter 3 Data Warehouse and OLAP Technology: An Overview data by OLAP operations), and data mining (which supports knowledge discovery).OLAP-based data mining is referred to as OLAP mining, or...
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Data Mining Concepts and Techniques phần 4 potx
... include data cube–based data aggregation and attribute-oriented induction.From a data analysis point of view, data generalization is a form of descriptive data mining. Descriptive data mining ... mining describes data in a concise and summarative manner and presents interesting general properties of the data. This is different from predic-tive data mining, which analyzes data in order to ... way, theproblem of mining frequent patterns in databases is transformed to that of mining theFP-tree.228 Chapter 5 Mining Frequent Patterns, Associations, and Correlationsdatabases. We begin...
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Oracle Data Guard Concepts and Administration
... Physical Standby and Cascaded Remote Physical Standby C-5C.3.2 Local Physical Standby and Cascaded Remote Logical Standby C-5C.3.3 Local and Remote Physical Standby and Cascaded Local Logical Standby ... is applied to the standby database.A standby database can be one of two types: a physical standby database or a logical standby database. If needed, either type of standby database can assume ... Physical and Logical Standby Databases■New Features Specific to Physical Standby Databases■New Features Specific to Logical Standby DatabasesNew Features Common to Physical and Logical Standby DatabasesThe...
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Oracle9i Data Mining Concepts Release 9.2.0.2 October 2002 Part No. A95961-02 Oracle9i Data
... SQL/MM for Data Mining. JDM has also influenced these standards. Oracle9i Data Mining will comply with the JDM standard when that standard is published.1.2.2 Data Mining ServerThe Data Mining ... viii Basic ODM Concepts 1-11Basic ODM Concepts Oracle9i Data Mining (ODM) embeds data mining within the Oracle9i database. The data never leaves the database — the data, data preparation, ... main components:■Oracle9i Data Mining API ■ Data Mining Server (DMS)1.2.1 Oracle9i Data Mining APIThe Oracle9i Data Mining API is the component of Oracle9i Data Mining that allows users to...
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Data Mining Classification: Alternative Techniques - Lecture Notes for Chapter 5 Introduction to Data Mining pdf
... 0 0 0 1vsd = 1.4142 d = 1.4142 • Solution: Normalize the vectors to unit length© Tan,Steinbach, Kumar Introduction to Data Mining 5 Rule Coverage and AccuracyCoverage of a rule:–Fraction ... by R1© Tan,Steinbach, Kumar Introduction to Data Mining 36 Instance Based ClassifiersExamples:–Rote-learner• Memorizes entire training data and performs classification only if attributes ... $10K to $1M© Tan,Steinbach, Kumar Introduction to Data Mining 40 1 nearest-neighborVoronoi Diagram© Tan,Steinbach, Kumar Introduction to Data Mining 38 Nearest-Neighbor ClassifiersRequires...
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Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining pptx
... Married 80K ? 10 Test Data © Tan,Steinbach, Kumar Introduction to Data Mining 25 Splitting Based on Continuous Attributes© Tan,Steinbach, Kumar Introduction to Data Mining 46 Tree InductionGreedy ... Tan,Steinbach, Kumar Introduction to Data Mining 3 Illustrating Classification Task© Tan,Steinbach, Kumar Introduction to Data Mining 13 Apply Model to Test Data RefundMarStTaxIncYESNONONOYes ... AttributesTraining Data Model: Decision Tree© Tan,Steinbach, Kumar Introduction to Data Mining 8 Decision Tree Classification TaskDecision Tree© Tan,Steinbach, Kumar Introduction to Data Mining 35...
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Data Mining Association Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 6 Introduction to Data Mining pdf
... Specific-to-general Data Mining Association Analysis: Basic Concepts and AlgorithmsLecture Notes for Chapter 6Introduction to Data Mining byTan, Steinbach, Kumar© Tan,Steinbach, Kumar Introduction to Data ... to Data Mining 49 Effect of Support DistributionMany real data sets have skewed support distributionSupport distribution of a retail data set© Tan,Steinbach, Kumar Introduction to Data Mining ... may decouple the support and confidence requirements© Tan,Steinbach, Kumar Introduction to Data Mining 16 Reducing Number of ComparisonsCandidate counting:–Scan the database of transactions...
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Data Mining Association Rules: Advanced Concepts and Algorithms Lecture Notes for Chapter 7 Introduction to Data Mining docx
... Introduction to Data Mining 23 Multi-level Association RulesHow do support and confidence vary as we traverse the concept hierarchy?–If X is the parent item for both X1 and X2, then σ(X) ... 0.33 0.00 0.33Normalize© Tan,Steinbach, Kumar Introduction to Data Mining 31 Sequential Pattern Mining: DefinitionGiven: –a database of sequences –a user-specified minimum support threshold, ... found–Candidate Generation: •Merge pairs of frequent subsequences found in the (k-1)th pass to generate candidate sequences that contain k items –Candidate Pruning:•Prune candidate k-sequences...
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Data Mining Cluster Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining pot
... clusters are irregular or intertwined, and when noise and outliers are present. 6 density-based clusters© Tan,Steinbach, Kumar Introduction to Data Mining 34 Handling Empty ClustersBasic K-means ... Tan,Steinbach, Kumar Introduction to Data Mining 42 Limitations of K-means: Non-globular ShapesOriginal PointsK-means (2 Clusters)© Tan,Steinbach, Kumar Introduction to Data Mining 13 Types of Clusters: ... Kumar Introduction to Data Mining 5 Notion of a Cluster can be AmbiguousHow many clusters?Four Clusters Two Clusters Six Clusters © Tan,Steinbach, Kumar Introduction to Data Mining 41 Limitations...
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Data Mining Cluster Analysis: Advanced Concepts and Algorithms Lecture Notes for Chapter 9 Introduction to Data Mining pot
... to Data Mining 11 Sparsification in the Clustering Process© Tan,Steinbach, Kumar Introduction to Data Mining 22 Experimental Results: CHAMELEON© Tan,Steinbach, Kumar Introduction to Data Mining ... Kumar Introduction to Data Mining 18 Experimental Results: CHAMELEON© Tan,Steinbach, Kumar Introduction to Data Mining 16 Chameleon: StepsPreprocessing Step: Represent the Data by a Graph–Given ... cluster the data is drastically reduced–The size of the problems that can be handled is increased © Tan,Steinbach, Kumar Introduction to Data Mining 35 SNN Clustering Can Handle Other...
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Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management - Second Edition
... of techniques to apply in a particular situation depends on the nature of the data mining task, the nature of the available data, and the skills and preferences of the data miner. Data mining ... By data mining, of course! How Data Mining Was Applied Most data mining methods learn by example. The neural network or decision tree generator or what have you is fed thousands and thousands ... that, on a technical level, the data mining effort is working and the data is reasonably accurate. This can be quite comforting. If the data and the data mining techniques applied to it are powerful...
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