Data Mining Concepts and Techniques phần 2 ppsx

... means:Bin 1: 9, 9, 9Bin 2: 22 , 22 , 22 Bin 3: 29 , 29 , 29 Smoothing by bin boundaries:Bin 1: 4, 4, 15Bin 2: 21 , 21 , 24 Bin 3: 25 , 25 , 34Figure 2. 11 Binning methods for data smoothing.1. Binning: ... forsmeared data. 2. 3 Data Cleaning 63Sorted data for price (in dollars): 4, 8, 15, 21 , 21 , 24 , 25 , 28 , 34Partition into (equal-frequency) bins:Bin 1: 4, 8, 15Bin 2: 21 , 21 , 24 Bin 3: 25 , 28 , 34Smoothing ... Using Equation (2. 9)for χ 2 computation, we getχ 2 = (25 0−90) 2 90+(50 21 0) 2 210+ (20 0−360) 2 360+(1000−840) 2 840= 28 4.44+ 121 .90+ 71.11+ 30.48 = 507.93.For this 2 × 2 table, the degrees...

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 ... time-series streams, spatiotemporal data streams, and video and audio data streams.8 .2 Mining Time-Series Data “What is a time-series database?” A time-series database consists of sequences of ... continue to grow and become more accurate as more training data stream in.8 .2 Mining Time-Series Data 489user-speciﬁed time horizon, h, and two clock times, t1 and t 2 (where t1< t 2 ), clusterevolution...

Data Mining Concepts and Techniques phần 1 potx

... Values 61 2. 3 .2 Noisy Data 62 2.3.3 Data Cleaning as a Process 65 2. 4 Data Integration and Transformation 67 2. 4.1 Data Integration 67 2. 4 .2 Data Transformation 70 2. 5 Data Reduction 72 2.5.1 Data ... Descriptive Data Summarization 51 2. 2.1 Measuring the Central Tendency 51 2. 2 .2 Measuring the Dispersion of Data 53 2. 2.3 Graphic Displays of Basic Descriptive Data Summaries 56 2. 3 Data Cleaning 61 2. 3.1 ... 66511.3 .2 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 Concepts and Techniques phần 3 docx

... dimensions city, item, and year.4.1 Efﬁcient Methods for Data Cube Computation 177a1:3 2 b*:13b1 :2 c*:14d*:15c* :2 d* :2 b*:13c*:14BCD:11root:51a 2 :2 b* :2 c3 :2 d4 :2 d*:15a1CD/a1:1 ... 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 ... memory.1 52 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, ...

Data Mining Concepts and Techniques phần 4 potx

... count sales countAsia 15 300 120 1000 135 1300Europe 12 250 150 120 0 1 62 1450NorthAmerica 28 450 20 0 1800 22 8 22 50all regions 45 1000 470 4000 525 5000Generalized data can be presented graphically, ... Lk−1 (2) for each itemset l 2 ∈ Lk−1(3) if (l1[1] = l 2 [1]) ∧(l1 [2] = l 2 [2] ) ∧ ∧(l1[k 2] = l 2 [k 2] )∧(l1[k −1] < l 2 [k −1]) then {(4) c = l1on l 2 ; // join step: generate candidates(5) ... {{I2, I1: 2} , {I2: 2} ,{I1: 2} }. Its conditional FP-tree has two branches, I2: 4, I1: 2 and I1: 2 , as shown inFigure 5.8, which generates the set of patterns, {{I2, I3: 4}, {I1, I3: 4}, {I2,...

Data Mining Concepts and Techniques phần 5 ppt

... coverage(R1) = 2/ 14 = 14 .28 % and accuracy(R1) = 2/ 2 = 100%. 29 6 Chapter 6 Classiﬁcation and Predictionthat satisfy the test. The right branch out of N is labeled no so that D 2 correspondsto ... obtainSplitInfoA(D) = −414×log 2 414−614×log 2 614−414×log 2 414.= 0. 926 .From Example 6.1, we have Gain(income) = 0. 029 . Therefore, GainRatio(income) =0. 029 /0. 926 = 0.031.6.6 Classiﬁcation ... 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

... asymmetric binary variables.1 2 235433 2 1x 2 = (3,5)x1 = (1 ,2) Euclidean distance= (2 2 + 3 2 )1 /2 = 3.61Manhattan distance= 2 + 3 = 5Figure 7.1 Euclidean and Manhattan distances ... asd(i, j) =w1|xi1−xj1| 2 + w 2 |xi2−xj2| 2 + ···+ wm|xin−xjn| 2 . (7.8)Weighting can also be applied to the Manhattan and Minkowski distances.7 .2. 2 Binary VariablesLet us see ... =err(M1) −err(M 2 )var(M1−M 2 )/k, (6.68)wherevar(M1−M 2 ) =1kk∑i=1err(M1)i−err(M 2 )i−(err(M1) −err(M 2 )) 2 . (6.69)To determine whether M1 and M 2 are signiﬁcantly...

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 ... proposed by Chandrasekaran and Franklin[CF 02] , Gehrke, Korn, and Srivastava [GKS01], Dobra, Garofalakis, Gehrke, and Ras-togi [DGGR 02] , and Madden, Shah, Hellerstein, and Raman [MSHR 02] . A one-passsummary ... [OMM+ 02] . Aggarwal, Han,Wang, and Yu proposed CluStream, a framework for clustering evolving data streams5 62 Chapter 9 Graph Mining, Social Network Analysis, and Multirelational Data Mining another...

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 ... t1t 2 t3t4t5t6t7d10 4 10 8 0 5 0d 2 5 19 7 16 0 0 32 d315 0 0 4 9 0 17d4 22 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 can ... 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 Mining Concepts and Techniques phần 10 pot

... molecules. In Proc. 20 02 Int. Conf. Data Mining (ICDM’ 02) , pages 21 1 21 8,Maebashi, Japan, Dec. 20 02. [BBD+ 02] B. Babcock, S. Babu, M. Datar, R. Motwani, and J. Widom. Models and issues in data stream ... streaming data. In Proc. 20 02 Int. Conf. Very Large Data Bases (VLDB’ 02) , pages 20 3 21 4, Hong Kong, China,Aug. 20 02. [CF03] C. Cooper and A Frieze. A general model of web graphs. Algorithms, 22 :311–335, ... 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...

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