Data Mining Concepts and Techniques phần 3 docx

... Kimball and Ross [KR02], Imhoff, Galemmo, and Geiger [IGG 03] , Inmon [Inm96], Berson and Smith [BS97b], and Thomsen [Tho97]. 134 Chapter 3 Data Warehouse and OLAP Technology: An Overview 3. 3 .3 Data ... 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 ... dedicated to data warehousingtechnology. 3. 3.4 Metadata RepositoryMetadata are data about data. When used in a data warehouse, metadata are the data thatdeﬁne warehouse objects. Figure 3. 12 showed...

Data Mining Concepts and Techniques phần 1 potx

... QueryingMultidimensional Databases 126 3. 3 Data Warehouse Architecture 127 3. 3.1 Steps for the Design and Construction of Data Warehouses 128 3. 3.2 A Three-Tier Data Warehouse Architecture 130 3. 3 .3 Data Warehouse ... 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 ... Tools and Utilities 134 3. 3.4 Metadata Repository 134 3. 3.5 Types of OLAP Servers: ROLAP versus MOLAPversus HOLAP 135 3. 4 Data Warehouse Implementation 137 3. 4.1 Efﬁcient Computation of Data...

Data Mining Concepts and Techniques phần 2 ppsx

... both data warehousing and data mining, as real-world data tend to be incomplete, noisy, and inconsistent. Data preprocessingincludes data cleaning, data integration, data transformation, and data ... noise, and inconsistencies contribute to inaccurate data. So far, we havelooked at techniques for handling missing data and for smoothing data. “But data clean-ing is a big job. What about data ... are constructed and added from the given set of attributes to help the mining process.Smoothing is a form of data cleaning and was addressed in Section 2 .3. 2. Section 2 .3. 3on the data cleaning...

Data Mining Concepts and Techniques phần 4 potx

... data mining. Descriptive data 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, ... (d1,b2, d 3 , d4, , d9, d10), and (3) (d1, d2, c 3 , d4, , d9, d10), wherea1= d1, b2= d2, and c 3 = d 3 . The measure of the cube is count.220 Chapter 4 Data Cube ... levels. Data generalization approaches 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. ...

Data Mining Concepts and Techniques phần 5 ppt

... credit_ratingexcellentexcellentexcellentfair RID124 3 age26 35  38 49 RID2 3 14 RID12 3 4 node52 3 6 buys_computeryesyesnono 01 3 5 642Disk-resident attribute ... list and class list data structures used in SLIQ for the tuple data of Table 6.2.credit_ratingexcellentexcellentexcellentfair age26 35  38 49 RID2 3 14 RID124 3 ... prediction in data mining. 32 4 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 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 ... described inPreparata and Shamos [PS85]. References on case-based reasoning (CBR) include thetexts Riesbeck and Schank [RS89] and Kolodner [Kol 93] , as well as Leake [Lea96] and Aamodt and Plazas [AP94]. ... test -3: 6.16 Summary 37 30.00.0 0.2 0.4 0.6 0.8 1.00.20.40.60.81.0true positive ratefalse positive rateFigure 6 .33 The ROC curves of two classiﬁcation models.6.16SummaryClassiﬁcation and...

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 ... 0.10C 0.16 0 .36 0.28 0.20G 0.15 0 .35 0 .36 0.14T 0.09 0 .37 0 .36 0.18(8.11)CpG−=A C G TA 0.27 0.19 0 .31 0. 23 C 0 .33 0 .31 0.08 0.28G 0.26 0.24 0 .31 0.19T 0.19 ... omitted here). 532 Chapter 8 Mining Stream, Time-Series, and Sequence Data There have been extensive studies on stream data management and the processingof continuous queries in stream data. For a...

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 638 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 ... multiple data mining systems and functions, and promote the education and use of data mining systems in industry and society.Recent efforts in this direction include Microsoft’s OLE DB for Data Mining...

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