Data Mining Concepts and Techniques phần 1 potx

... 665 11 .3.2 Statistical Data Mining 666 11 .3.3 Visual and Audio Data Mining 667 11 .3.4 Data Mining and Collaborative Filtering 670 11 .4 Social Impacts of Data Mining 675 11 .4 .1 Ubiquitous and ... Prototypes 660 11 .2 .1 How to Choose a Data Mining System 660 11 .2.2 Examples of Commercial Data Mining Systems 663 11 .3 Additional Themes on Data Mining 665 11 .3 .1 Theoretical Foundations of Data Mining ... Data Mining 675 11 .4 .1 Ubiquitous and Invisible Data Mining 675 11 .4.2 Data Mining, Privacy, and Data Security 678 11 .5 Trends in Data Mining 6 81 11. 6 Summary 684Exercises 685Bibliographic Notes...

Data Mining Concepts and Techniques phần 4 potx

... minsup} (10 ) } (11 ) return L = ∪kLk;procedure apriori gen(Lk 1 :frequent (k 1) -itemsets) (1) for each itemset l 1 ∈ Lk 1 (2) for each itemset l2∈ Lk 1 (3) if (l 1 [1] = l2 [1] ) ∧(l 1 [2] ... d9, d 10 ), and (3) (d 1 , d2, c3, d4, , d9, d 10 ), wherea 1 = d 1 , b2= d2, and c3= d3. The measure of the cube is count.220 Chapter 4 Data Cube Computation and Data Generalization(a) ... computer 15 0 12 00NorthAmerica computer 200 18 00Table 4 .15 A crosstab for the sales in 2004.itemTV computer both itemslocation sales count sales count sales countAsia 15 300 12 0 10 00 13 5 13 00Europe...

Data Mining Concepts and Techniques phần 8 potx

... 0 .10 C 0 .16 0.36 0.28 0.20G 0 .15 0.35 0.36 0 .14 T 0.09 0.37 0.36 0 .18 (8 .11 )CpG−=A C G TA 0.27 0 .19 0. 31 0.23C 0.33 0. 31 0.08 0.28G 0.26 0.24 0. 31 0 .19 T ... 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 ... π 1 π2 πL,πL +1 = 0. The probability that the model generated sequence x using path π isP(x, π) = a0π 1 eπ 1 (x 1 ) ·aπ 1 π2eπ2(x2) ····aπL 1 πLeπL(xL) ·aπL0(8 .13 )=...

Data Mining Concepts and Techniques phần 2 ppsx

... getχ2=(250−90)290+(50− 210 )2 210 +(200−360)2360+ (10 00−840)2840= 284.44+ 12 1.90+ 71. 11+ 30.48 = 507.93.For this 2 × 2 table, the degrees of freedom are (2 1) (2 1) = 1. For 1 degree offreedom, ... approximately 10 0 fi% of the data are below or equal to the value, xi. We say “approximately” because2.5 Data Reduction 832520 15  10 50 1 10 11 –20 21 30price ($)countFigure 2 .19 An equal-width ... Descriptive Data Summarization 5760005000400030002000 10 000Count of items sold40–5960–79 80–99 10 0 11 9 12 0 13 9Unit Price ($)Figure 2.4 A histogram for the data set of Table 2 .1. Table 2 .1 A...

Data Mining Concepts and Techniques phần 3 docx

... item, and year.4 .1 Efﬁcient Methods for Data Cube Computation 17 7a 1 :32b* :1 3b 1 :2c* :1 4d* :1 5c*:2d*:2b* :1 3c* :1 4BCD :1 1root:5 1 a2:2b*:2c3:2d4:2d* :1 5a 1 CD/a 1 :1 a 1 b*D/a 1 b* :1 3d* :1 5a 1 b*c*/a 1 b*c* :1 4Base–Tree ... quarter year dollars sold 10 01 TV 15 10 Q4 2003 250.60 10 02 TV 23 10 Q4 2003 17 5.00 . 50 01 TV all 10 Q4 2003 45,786.08 . 15 8 Chapter 4 Data Cube Computation and Data Generalizationspace. ... 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...

Data Mining Concepts and Techniques phần 5 ppt

... onthis partitioning isGiniincome ∈ {low,medium}(D)= 10 14 Gini(D 1 ) +4 14 Gini(D2)= 10 14  1 6 10 2−4 10 2+4 14  1  1 42−342= 0.450= Giniincome ∈ {high}(D).Similarly, ... Oj= 1 1+e−Ij; }// compute the output of each unit j (10 ) // Backpropagate the errors: (11 ) for each unit j in the output layer (12 ) Errj= Oj (1 Oj)(Tj−Oj); // compute the error (13 ) ... 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, GainRatio(income) =0.029/0.926 = 0.0 31. 6.6 Classiﬁcation...

Data Mining Concepts and Techniques phần 6 ppt

... follows:t =err(M 1 ) −err(M2)var(M 1 −M2)/k, (6.68)wherevar(M 1 −M2) = 1 kk∑i =1 err(M 1 )i−err(M2)i−(err(M 1 ) −err(M2))2. (6.69)To determine whether M 1 and M2are ... estimatedasvar(M 1 −M2) =var(M 1 )k 1 +var(M2)k2, (6.70) and k 1 and k2are the number of cross-validation samples (in our case, 10 -fold cross-validation rounds) used for M 1 and M2, ... asymmetric binary variables. 1 22354332 1 x2 = (3,5)x 1 = (1, 2)Euclidean distance= (22 + 32) 1/ 2 = 3. 61 Manhattan distance= 2 + 3 = 5Figure 7 .1 Euclidean and Manhattan distances...

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

... t 1 t2t3t4t5t6t7d 1 0 4 10 8 0 5 0d25 19 7 16 0 0 32d3 15 0 0 4 9 0 17 d422 3 12 0 5 15 0d50 7 0 9 2 4 12 634 Chapter 10 Mining Object, Spatial, Multimedia, Text, and Web Data These equations ... 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 ... results6www.yahooligans.com/content/animals 10 .3 Multimedia Data Mining 611 Figure 10 .5 An output of the Classiﬁer module of MultiMediaMiner. 10 .3.3 Classiﬁcation and Prediction Analysis of Multimedia Data Classiﬁcation and predictive...

Data Mining Concepts and Techniques phần 10 pot

... multidimen-sional databases. SIGMOD Record, 26 :12 17 , 19 97.[BS97b] A.BersonandS.J.Smith.DataWarehousing,DataMining,andOLAP.McGraw-Hill ,19 97.[BST99] A. Berson, S. J. Smith, and K. Thearling. Building Data Mining ... analysis, multirelational and multidatabase data mining, data privacyprotection, and data security.Exercises 11 .1 Research anddescribe an application of datamining that wasnot presented inthis chapter.Discuss ... data sources, data mining functions, data mining methodologies, couplingwith database or data warehouse systems, scalability, visualization tools, and graphical682 Chapter 11 Applications and...

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