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 ... primitive-level data. For instance, if “IBM-ThinkPad-R40/P4M” or “Symantec-Norton-Antivirus-2003” each212 Chapter 4 Data Cube Computation and Data GeneralizationExample 4. 27 Mining a class comparison. ... graphs is popular in data analysis. Such graphs and curves canrepresent 2-D or 3-D data. Example 4. 24 Bar chart and pie chart. The sales data of the crosstab shown in Table 4. 15 can be trans-formed...

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 ... Time-Series, and Sequence Data 46 78.1 Mining Data Streams 46 88.1.1 Methodologies for Stream Data Processing and Stream Data Systems 46 98.1.2 Stream OLAP and Stream Data Cubes 47 48.1.3 Frequent-Pattern ... 1353 .4 Data Warehouse Implementation 1373 .4. 1 Efﬁcient Computation of Data Cubes 1373 .4. 2 Indexing OLAP Data 141 3 .4. 3 Efﬁcient Processing of OLAP Queries 144 3.5 From Data Warehousing to Data Mining...

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 ... caffeine and thesal inFigure 9. 14( a) and 9. 14( b) will be good matches. If we relax the query further, the struc-ture in Figure 9. 14( c) could also be an answer. 544 Chapter 9 Graph Mining, Social ... of sequence data include cus-tomer shopping sequences, Web clickstreams, and biological sequences. 548 Chapter 9 Graph Mining, Social Network Analysis, and Multirelational Data Mining graph....

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, ... forsmeared data. 2.3 Data Cleaning 63Sorted data for price (in dollars): 4, 8, 15, 21, 21, 24, 25, 28, 34 Partition into (equal-frequency) bins:Bin 1: 4, 8, 15Bin 2: 21, 21, 24 Bin 3: 25, 28, 34 Smoothing ... 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...

Data Mining Concepts and Techniques phần 3 docx

... Generalizationb3b2b1b0BCc3c2c161 45 2962 46 3063 47 31 64 4832c0a0a1Aa2a315913 14 15 162 3 4 28 44 60 24 4056203652Figure 4. 3 A 3-D array for the dimensions A, B, and C, organized into 64 chunks. Each chunk ... 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 5 ppt

... isGiniincome ∈ {low,medium}(D)=10 14 Gini(D1) + 4 14 Gini(D2)=10 14 1−6102− 4 102+ 4 14 1−1 4 2−3 4 2= 0 .45 0= Giniincome ∈ {high}(D).Similarly, ... income, we ﬁrst use Equation (6.5)to obtainSplitInfoA(D) = − 4 14 ×log2 4 14 −6 14 ×log26 14 − 4 14 ×log2 4 14 .= 0.926.From Example 6.1, we have Gain(income) = 0.029. Therefore, ... 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...

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 ... inCooper and Herskovits [CH92], Buntine [Bun 94] , and Heckerman, Geiger, and Chick-ering [HGC95]. Algorithms for inference on belief networks can be found in Russell and Norvig [RN95] and Jensen ... inPreparata and Shamos [PS85]. References on case-based reasoning (CBR) include thetexts Riesbeck and Schank [RS89] and Kolodner [Kol93], as well as Leake [Lea96] and Aamodt and Plazas [AP 94] . For...

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 9 pot

... multimedia data mining focuses on image data mining. Mining text data and mining the World Wide Web are studied in the two subsequent638 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 ... intime-series databases. In Proc. 19 94 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’ 94) , pages 41 9 42 9, Minneapolis, MN, May 19 94. [FS93] U. Fayyad and P. Smyth. Image database exploration: progress and ... 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...

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