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Data Mining and Knowledge Discovery Handbook, 2 Edition part 6 ppt

Data Mining and Knowledge Discovery Handbook, 2 Edition part 6 ppt

Data Mining and Knowledge Discovery Handbook, 2 Edition part 6 ppt

... Data Warehousing and Knowledge Discovery; 20 02 September 04- 06; 170-180.Hernandez, M. & Stolfo, S. Real-world Data is Dirty: Data Cleansing and The Merge/PurgeProblem, Data Mining and Knowledge ... Headache Nausea Flu1 100 .2 yes no yes 2 1 02. 6 yes yes yes3 99 .2 no no no4 99 .6 yes yes yes5 99.8 yes yes no 6 96. 4 yes no no7 96. 6 no yes no8 99 .2 yes yes yes 2 Data Cleansing 31Knorr, ... Methods, Data Mining and Knowledge Discov-ery Handbook, Springer, pp. 321 -3 52. Simoudis, E., Livezey, B., & Kerber, R., Using Recon for Data Cleaning. In Advances in Knowledge Discovery and Data...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 3 pptx

Data Mining and Knowledge Discovery Handbook, 2 Edition part 3 pptx

... of data (though of differenttypes and reliability). The internet and intranet fast development in particular pro-O. Maimon, L. Rokach (eds.), Data Mining and Knowledge Discovery Handbook, 2nd ... understanding phenomena from the data, analysis and prediction.The accessibility and abundance of data today makes Knowledge Discovery and Data Mining a matter of considerable importance and necessity. ... means), and analysis of variance (ANOVA). These methods areless associated with Data Mining than their discovery- oriented counterparts, because1 Introduction to Knowledge Discovery and Data Mining...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 5 pptx

Data Mining and Knowledge Discovery Handbook, 2 Edition part 5 pptx

... analyze, and investigate such very large data sets hasgiven rise to the fields of Data Mining (DM) and data warehousing (DW). Withoutclean and correct data the usefulness of Data Mining and data ... experimental data set, 164 contain outlier 2 Data Cleansing 29 Table 2. 2. A part of the data set. An error was identified in record 199, field 14, which wasnot identified previously. The data elements ... Manage-ment of Data; 1993 May; Washington D.C. 20 7 -21 6. 20 Jonathan I. Maletic and Andrian Marcusprocess of data cleansing is also laborious, time consuming, and itself prone to errors.Useful and powerful...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 10 ppt

Data Mining and Knowledge Discovery Handbook, 2 Edition part 10 ppt

... latter can be reduced to O(hm 2 logm) where h isa heap size (Silva and Tenenbaum, 20 02) . Landmark Isomap simply employs land-mark MDS (Silva and Tenenbaum, 20 02) to addresses this problem, ... are sparse and can therefore beeigendecomposed efficiently. 72 Christopher J.C. Burgesm∑i=1ˆyi−yi 2 =1 2 m∑i=1ˆxi−xi 2 =1 2 m∑i=1p∑a=1λa˜e(i )2 a+1 2 m∑i=1r∑a=1λa˜e(i )2 a−m∑i=1p∑a=1λa˜e(i )2 abut∑mi=1˜e(i )2 a=∑mi=1e(a )2 i= ... coordinates, we have b −1 2 (f−¯E), and the coordinatesof the embedded test point are1 2 Λ−1 /2 U(¯E −f); this reproduces the form given in(Silva and Tenenbaum, 20 02) . Landmark MDS has two...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 20 ppt

Data Mining and Knowledge Discovery Handbook, 2 Edition part 20 ppt

... Number 2, 20 05b, pp 131–158.Rokach, L. and Maimon, O., Clustering methods, Data Mining and Knowledge Discovery Handbook, pp. 321 –3 52, 20 05, Springer.Rokach, L. and Maimon, O., Data mining for ... for Data Mining, Proc. 22 nd Int. Conf. Very Large Databases, T. M. Vijayaraman and AlejandroP. Buchmann and C. Mohan and Nandlal L. Sarda (eds), 544-555, Morgan Kaufmann,19 96. Sklansky, J. and ... pp. 169 -180, 1999.Gehrke J., Ramakrishnan R., Ganti V., RainForest - A Framework for Fast Decision TreeConstruction of Large Datasets ,Data Mining and Knowledge Discovery, 4, 2/ 3) 127 -1 62 , 20 00.Gelfand...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 24 ppt

Data Mining and Knowledge Discovery Handbook, 2 Edition part 24 ppt

... definitions of Data Mining as there are treatises on the sub-ject (Sutton and Barto, 1999, Cristianini and Shawe-Taylor, 20 00, Witten and Frank, 20 00,Hand et al., 20 01,Hastie et al., 20 01,Breiman, 20 01b,Dasu ... 20 01b,Dasu and Johnson, 20 03), and associated with Data Mining are a variety of names: statistical learning, machinelearning, reinforcement learning, algorithmic modeling and others. By Data Min-ing” ... =(β0−β 2 xa)+(β1+β 2 )x. (11 .6) Ifβ 2 is positive, for x ≥ a the line is more steep with a slope of (β1+β 2 ), and lower intercept of (β0−β 2 xa).Ifβ 2 is negative, the reverse holds.For...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 25 pptx

Data Mining and Knowledge Discovery Handbook, 2 Edition part 25 pptx

... Criminology and Public Policy, 2, No. 2: 21 5 -24 2.Breiman, L., Friedman, J.H., Olshen, R.A., and C.J. Stone, (1984) Classification and Regres-sion Trees. Monterey, Ca: Wadsworth Press.Breiman, L. (19 96) ... Learning 26 : 123 -140.Breiman, L. (20 00) “Some Infinity Theory for Predictor Ensembles.” Technical Report 522 ,Department of Statistics, University of California, Berkeley, California.Breiman, L. (20 01a) ... into an upper and lower part. Theupper left partition and the lower right partition are perfectly homogeneous. Thereremains considerable heterogeneity in the other two partitions and in principle,...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 38 pptx

Data Mining and Knowledge Discovery Handbook, 2 Edition part 38 pptx

... association rules. Data Mining and Knowledge Discovery, 2( 2):195 22 4, 1998.T. Mitchell. Generalization as search. Artificial Intelligence, 18 (2) :20 3 22 6, 1980.R. Ng, L. V. Lakshmanan, J. Han, and A. Pang. ... IDEAS’01,pages 322 – 329 , 20 01.C. Bucila, J. E. Gehrke, D. Kifer, and W. White. Dualminer: A dual-pruning algorithm foritemsets with constraints. Data Mining and Knowledge Discovery, 7(4) :24 1 27 2, 20 03.D. ... (eds.), Data Mining and Knowledge Discovery Handbook, 2nd ed., DOI 10.1007/978-0-387-09 823 -4_18, © Springer Science+Business Media, LLC 20 10 17 Constraint-based Data Mining 353F. Bonchi and C....
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 61 pptx

Data Mining and Knowledge Discovery Handbook, 2 Edition part 61 pptx

... %1 10, 3 26 9,9 72 0 354 99. 72 30K 12s. 2 11,751 0 10,000 1,751 1003 7, 923 28 0 7,895 78.951 103,331 99, 868 0 3, 463 99. 86 300K 56s. 2 117 ,29 7 0 100,000 17 ,29 7 1003 79,3 72 1 32 0 79 ,24 0 79 .24 1 1,033,795 ... 1,033,795 998 ,6 32 0 35, 163 99. 86 3M 485s. 2 1,1 72, 895 0 999,999 173,8 96 99.993 793,310 1, 368 0 791,9 42 79.191 10,335, 024 9,9 86, 110 22 348,897 99. 86 30M 4,987s. 2 11, 722 ,887 0 9,999,970 1, 722 ,917 99.993 ... and G. Hulten. Mining High-Speed Data Streams. In Proceedings of the SixthACM-SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA, 20 00.C. Faloutsos and V. Gaede....
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 65 ppt

Data Mining and Knowledge Discovery Handbook, 2 Edition part 65 ppt

... in Data Mining 62 1 31.3.1 Association Rules Interestingness MeasuresLet LHS → RHS be an association rule. Further we refer to the left hand side and the righthand side of the rule as LHS and ... P and Pcmatrices respectively. The CPCC arebetween −1 and 1. A value of the index close to 1 is an indication of a significant similarity31 Quality Assessment Approaches in Data Mining 62 7 Now ... between C and P:1. Rand Statistic: R =(a + d)/M 2. Jaccard Coefficient: J = a/(a+ b + c) The above two indices range between 0 and 1, and are maximized when m=s. Another index is the:3. Folkes and...
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