data mining concepts and techniques 3nd edition solution manual
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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Data Mining Concepts and Techniques phần 5 ppt
... to as training tuples and are selected from the database under analysis. In thecontext of classification, data tuples can be referred to as samples, examples, instances, data points, or objects.2Because ... scalability.While both SLIQandSPRINThandle disk-resident data sets thatare too large to fit intomemory, the scalabilityof SLIQ islimited by the useof its memory-residentdatastructure.SPRINT removes ... even for real-world data. RainForest has techniques, however, for handlingthe case where the AVC-group does not fit in memory. RainForest can use any attributeselection measure and was shown to...
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Data Mining Concepts and Techniques phần 6 ppt
... functions(Hanson and Burr [HB88]), dynamic adjustment of the network topology (Me´zard and Nadal [MN89], Fahlman and Lebiere [FL90], Le Cun, Denker, and Solla [LDS90], and Harp, Samad, and Guha [HSG90] ), and ... data in preparation for classification and prediction can involve data cleaning to reduce noise or handle missing values, relevance analysis to removeirrelevant or redundant attributes, and data ... the data. Early decision tree algorithms typi-cally assume that the data are memory resident—a limitation to data mining on largedatabases. Several scalable algorithms, such as SLIQ, SPRINT, and...
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Data Mining Concepts and Techniques phần 7 ppsx
... efficiently.8 Mining Stream, Time-Series, and Sequence Data Our previous chapters introduced the basic concepts and techniques of data mining. The techniques studied, however, were for simple and structured ... structured data sets, such as data in relationaldatabases, transactional databases, and data warehouses. The growth of data in variouscomplex forms (e.g., semi-structured and unstructured, spatial and ... telecommu-nications data, transaction data from the retail industry, and data from electric powergrids. Traditional OLAP and data mining methods typically require multiple scans ofthe data and are therefore...
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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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distributed systems concepts and design 5th edition
... repeaters and network interfaces; and software components,including protocol stacks, communication handlers and drivers. The resulting functionality and performance available to distributed system and ... devices Database serverApplicationlogicDatabasemanagerTier 1 Tier 2 Tier 3a) data manipulationApplication and data managementUser view,controls and data manipulation54 CHAPTER 2 SYSTEM ... extended and implemented in new ways without disturbing its existing functionality (see Section 1.5.2). First, its operation is based on communication standards and document or content standards...
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Hospitality strategic management concepts and cases (second edition)
... last edition, the business landscape in the hospitality indus-try has changed dramatically. Consolidation, mergers, new entrants, and new brand introductions continue, while tourism and brand ... the love and support of my family, who tolerated evenings and days spent working on this book. To my husband and children, I owe my gratitude for their constant encouragement and understanding. ... ResourcesFirm reputationBrand names and patentsContracts and licensesStakeholder relationshipsOrganizational Knowledge and LearningPhysical ResourcesLand and buildingsOrganizational...
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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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