Data Mining Concepts and Techniques phần 6 ppt

... test-3: 6. 16 Summary 3730.00.0 0.2 0.4 0 .6 0.8 1.00.20.40 .6 0.81.0true positive ratefalse positive rateFigure 6. 33 The ROC curves of two classiﬁcation models. 6. 16 SummaryClassiﬁcation and ... 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 ... such as Duda et al. [DHS01] and James [Jam85], as well as articles by Cover and Hart [CH67] and Fukunaga and Hummels [FH87]. Their integration with attribute-weighting and the pruning of noisy...

Data Mining Concepts and Techniques phần 5 ppt

... the tuples that it covers and see what percentage of them the rule can correctly classify.Example 6. 6Rule accuracy and coverage. Let’s go back to our data of Table 6. 1. These are class-labeledtuples ... The backpropagation algorithm is described inSection 6. 6.3. Rule extraction from trained neural networks is discussed in Section 6. 6.4. 6. 6.1 A Multilayer Feed-Forward Neural NetworkThe backpropagation ... prediction in data mining. 324 Chapter 6 Classiﬁcation and PredictionFigure 6. 13 A general-to-speciﬁc search through rule space.attribute tests such as att = val, att ≤ val, att > val, and so on....

Data Mining Concepts and Techniques phần 1 potx

... Mining 66 511.3.2 Statistical Data Mining 66 611.3.3 Visual and Audio Data Mining 66 711.3.4 Data Mining and Collaborative Filtering 67 011.4 Social Impacts of Data Mining 67 511.4.1 Ubiquitous and ... Prototypes 66 011.2.1 How to Choose a Data Mining System 66 011.2.2 Examples of Commercial Data Mining Systems 66 311.3 Additional Themes on Data Mining 66 511.3.1 Theoretical Foundations of Data Mining ... Ubiquitous and Invisible Data Mining 67 511.4.2 Data Mining, Privacy, and Data Security 67 811.5 Trends in Data Mining 68 111 .6 Summary 68 4Exercises 68 5Bibliographic Notes 68 7Appendix An Introduction...

Data Mining Concepts and Techniques phần 2 ppsx

... (inclusive).2.3 Data Cleaning 65 2.3.3 Data Cleaning as a ProcessMissing values, noise, and inconsistencies contribute to inaccurate data. So far, we havelooked at techniques for handling missing data and ... 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, ... The metadatamay also be used to help transform the data (e.g., where data codes for paytype in onedatabase may be “H” and “S”, and 1 and 2 in another). Hence, this step also relates to data cleaning,...

Data Mining Concepts and Techniques phần 3 docx

... and lacks ﬂexibility due to the difﬁculty in achieving 166 Chapter 4 Data Cube Computation and Data Generalizationb3b2b1b0BCc3c2c1 61 4529 62 46 30 63 4731 64 4832c0a0a1Aa2a315913 ... 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...

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 ... Subramanian and Feigenbaum [SF86b]. Overviews of machine learning techniques can be found in Dietterich and Michalski [DM83], Michalski, Carbonell, and Mitchell [MCM 86] , and Mitchell [Mit97].Database-oriented ... 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...

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...

Data Mining Concepts and Techniques phần 8 potx

... substructures.9. Metadata mining. Metadata are data about data. Metadata provide semi-structured data about unstructured data, ranging from text and Web data to multimedia data- bases. It is useful for data ... itemsetstream mining; the Hoeffding tree, VFDT, and CVFDT algorithms for stream data classiﬁcation; and the STREAM and CluStream algorithms for stream data clustering.A time-series database consists ... window size to use, and CpG islands tend to vary in length.What if, instead, we merge the two Markov chains from above (for CpG islands and non-CpG islands, respectively) and add transition probabilities...

Data Mining Concepts and Techniques phần 9 pot

... multimedia data mining focuses on image data mining. Mining text data and mining the World Wide Web are studied in the two subsequent 63 8 Chapter 10 Mining Object, Spatial, Multimedia, Text, and Web Data where ... closely linked to imageanalysis and scientiﬁc data mining, and thus many image analysis techniques and scien-tiﬁc data analysis methods can be applied to image data mining. The popular use of the ... data, and computertomography. It is important to explore datamining inraster or image databases.Methodsfor mining raster and image data are examined in the following section regarding themining...

Data Mining Concepts and Techniques phần 10 pot

... constraint-based mining) , the integration of data mining with data warehousing and database systems,the standardization of data mining languages, visualization methods, and new meth-ods for handling ... complex data types. Other trends include biological data mining, mining software bugs, Web mining, distributed and real-time mining, graph mining, social network analysis, multirelational and multidatabase ... data sources, data mining functions, data mining methodologies, couplingwith database or data warehouse systems, scalability, visualization tools, and graphical 68 2 Chapter 11 Applications and...

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