Ãëàâíàÿ    Ex Libris    Êíèãè    Æóðíàëû    Ñòàòüè    Ñåðèè    Êàòàëîã    Wanted    Çàãðóçêà    ÕóäËèò    Ñïðàâêà    Ïîèñê ïî èíäåêñàì    Ïîèñê    Ôîðóì   
blank
Àâòîðèçàöèÿ

       
blank
Ïîèñê ïî óêàçàòåëÿì

blank
blank
blank
Êðàñîòà
blank
Pearson R.K. — Mining imperfect data: dealing with contamination and incomplete records
Pearson R.K. — Mining imperfect data: dealing with contamination and incomplete records



Îáñóäèòå êíèãó íà íàó÷íîì ôîðóìå



Íàøëè îïå÷àòêó?
Âûäåëèòå åå ìûøêîé è íàæìèòå Ctrl+Enter


Íàçâàíèå: Mining imperfect data: dealing with contamination and incomplete records

Àâòîð: Pearson R.K.

Àííîòàöèÿ:

Data mining is concerned with the analysis of databases large enough that various anomalies, including outliers, incomplete data records, and more subtle phenomena such as misalignment errors, are virtually certain to be present. Mining Imperfect Data: Dealing with Contamination and Incomplete Records describes in detail a number of these problems, as well as their sources, their consequences, their detection, and their treatment. Specific strategies for data pretreatment and analytical validation that are broadly applicable are described, making them useful in conjunction with most data mining analysis methods. Examples are presented to illustrate the performance of the pretreatment and validation methods in a variety of situations; these include simulation-based examples in which "correct" results are known unambiguously as well as real data examples that illustrate typical cases met in practice.
Mining Imperfect Data, which deals with a wider range of data anomalies than are usually treated in one book, includes a discussion of detecting anomalies through generalized sensitivity analysis (GSA), a process of identifying inconsistencies using systematic and extensive comparisons of results obtained by analysis of exchangeable datasets or subsets. The book makes extensive use of real data, both in the form of a detailed analysis of a few real datasets and various published examples. Also included is a succinct introduction to functional equations that illustrates their utility in describing various forms of qualitative behavior for useful data characterizations.


ßçûê: en

Ðóáðèêà: Ìàòåìàòèêà/

Ñòàòóñ ïðåäìåòíîãî óêàçàòåëÿ: Ãîòîâ óêàçàòåëü ñ íîìåðàìè ñòðàíèö

ed2k: ed2k stats

Ãîä èçäàíèÿ: 2005

Êîëè÷åñòâî ñòðàíèö: 316

Äîáàâëåíà â êàòàëîã: 23.10.2010

Îïåðàöèè: Ïîëîæèòü íà ïîëêó | Ñêîïèðîâàòü ññûëêó äëÿ ôîðóìà | Ñêîïèðîâàòü ID
blank
Ïðåäìåòíûé óêàçàòåëü
$3\sigma$ edit rule      23 70 73—76 88 274
Akaike information criterion (AIC)      114
Autocorrelation      8 241
Auxiliary knowledge      138—139 222
Bagplot      204
Balanced      186
Binary variable      266—268
Biplots      205
Bootstrap      208 210
Bootstrap, moving blocks      210
Boxplot      26 178 193
Boxplot, asymmetric outlier rule      78
Boxplot, clockwise bivariate      205
Boxplot, definition and illustration      4—7
Boxplot, outlier rule      70 73 77—78 88
Boxplot, symmetric outlier rule      78
Breakdown, definition      19
Breakdown, finite sample      76
Breakdown, how high?      275
Breakdown, kurtosis estimator      20
Breakdown, masking      76
Breakdown, maximum possible      19
Breakdown, mean      20
Breakdown, median      19
Breakdown, swamping      77
Caliper      179
Canonical correlation analysis (CCA)      130
Chaos      168
Cluster analysis      97 174 183 192 230—231 276
Cluster analysis, balanced      231
Collinearity      58 97—100
Collinearity, definition      11
Computational negative controls (CNC)      219 244—251
Constraints      58—60
Contamination, definition      19
Contamination, typical      19
Continuity, absolute      194
Convex function      163
Convex polytope      169
Correlation coefficient      196
Correlation coefficient, overlapping subsets      212—213
Correlation coefficient, product-moment      47—48
Correlation coefficient, Spearman rank      50—51 197 248 280
Correlation coefficient, zero      165
Covariance matrix, definition      48
Covariance matrix, indefinite      108
Cross-correlation      241—244
Cross-correlation, rank-based      248—251
Crossing      187
Data depth      134—138 228
Data distribution, asymmetric      36 90 277—283
Data distribution, beta      232
Data distribution, chi-square ($\chi_p^2$)      129
Data distribution, exponential      278
Data distribution, gamma      194 278
Data distribution, Gaussian (normal)      74 219 278
Data distribution, Student’s t      233
Data distribution, uniform      167 194
Data distribution, unimodal      36
Data-based prototyping      270
Dataset, Brownlee      272
Dataset, CAMDA normal mouse      16—17 24 54 138—139 283
Dataset, catalyst      87—88
Dataset, flow rate      88—90
Dataset, industrial pressure      90—91 235—237 254—263
Dataset, Michigan lung cancer      263—268
Dataset, microarray      1 2 37—39 95—96 126
Dataset, storage tank      237—251
DataSpheres      228
Deadzone nonlinearity      65
Deletion diagnostics      30 213—217 241 244 274
Deletion diagnostics, successive      216
Descriptor      26 178 179 192—193
Descriptor, figure-of-merit      192 199—201
Distance-distance plot      129
Dotplot      193
Empirical quantiles      5
Equivariant      155
Equivariant, affine      156
Equivariant, regression      156
Equivariant, scale      156
Exact fit property (EFP)      141
Exchangeability      25 32 178—180 199 219
Expectation Maximization (EM) algorithm      203
Experimental design      188—190
Extended data sequence      254
Extreme studentized deviation (ESD)      74
Forward search      133
Fouling      243
Function differentiable      151 157
Functional equation      144—159
Functional equation, bisymmetry      154
Functional equation, Cauchy’s basic      144
Functional equation, Cauchy’s exponential      145
Functional equation, Cauchy’s logarithmic      145
Functional equation, Cauchy’s power      146
Galton’s skewness measure      196 278
Galton’s skewness measure, definition      44
Galton’s skewness measure, outlier-sensitivity      44
General position      156 158
Generalized sensitivity analysis (GSA)      25—31 177—201
Giant magnetostriction      13
Gini’s mean difference      280
Gross errors      33 52—53
Hampel filter      122—124
Hampel filter, iterative      202
Hampel identifier      24 35 70 73 76—77 88
Hard saturation nonlinearity      64
Heuristic      177
Homogeneity      134 144 147—149 151
Homogeneity, generalized      147
Homogeneity, order c      147
Homogeneity, order zero      149
Homogeneity, positive      147
Hotelling’s $T^2$ statistic      129
Ideal quantizer      64
Idempotent      9 60 62—66 118 120
Implosion      77
Imputation      23 60
Imputation, cold-deck      105
Imputation, hot-deck      105
Imputation, mean      103
Imputation, multiple      105—108 276
Imputation, single      103—105 276
Inequality      159—166
Inequality, arithmetic-geometric mean (AGM)      154 161—162
Inequality, Cauchy — Schwarz      160
Inequality, Chebyshev      159
Inequality, Jensen      163
Interquartile distance (IQD), comparisons      29—30
Interquartile distance (IQD), definition      7 24
Interquartile distance (IQD), outlier-sensitivity      43
Interval arithmetic      168
Inward testing procedures      202
Jackknife      214—215
Kurtosis variability      232—235
Kurtosis, beta distribution      232
Kurtosis, breakdown point      20
Kurtosis, definition      5
Kurtosis, estimator      5
Kurtosis, lower bound      18 232
Kurtosis, outlier-sensitivity      18
Kurtosis, Student’s t      234
Leptokurtic      233
Literary Digest      61
Location-invariance      149—152
Lowess smoother      223 254
MA plot      37 126
Mahalanobis distance      39 128—131
Mahalanobis distance, definition      47
Mann — Whitney test      46
Martin — Thomson data cleaner      115 275
Masking      74 274
Mean, arithmetic      154 157 161—162
Mean, bounds      163—166
Mean, generalized      161
Mean, geometric      154 161—162
Mean, harmonic      154 162 165
Mean, outlier-sensitivity      20 41—43
Mean, quasi-arithmetic      154
Mean, quasilinear      154—155 161
Mean, versus median      20—23
Mean, zero breakdown      20
Median absolute deviation (MAD), comparisons      29—30
Median absolute deviation (MAD), definition      24
Median Filter      117
Median filter, center-weighted (CWMF)      118—122 187
Median versus mean      20—23
Median, breakdown      19
Median, characterization      157
Median, definition      5
Median, deletion-sensitivity      21
Median, multivariate      134—138
Median, outlier-sensitivity      43
Median, smallest bias      275
Metaheuristic      177 178
Mice and elephants      211
Microarray      1 2 16—17 37 54
Midmean      28 256 275
Minimum covariance determinant (MCD)      128
Minimum volume ellipsoid (MVE)      131
Minkowski addition      168
Misalignment, CAMDA dataset      16—17 25
Misalignment, caused by missing data      55
Misalignment, caused by software errors      56
Misalignment, causes      54—58
Misalignment, consequences      9—11
Misalignment, definition      9
Misalignment, detection      283—286
Misalignment, prevalence      274
Missing data from file merging      66—67
Missing data, coded      33 71
Missing data, definition      7
Missing data, disguised      8 33 60 71
Missing data, ignorable      7 60
Missing data, imputation      see "Imputation"
Missing data, missing at random (MAR)      277
Missing data, missing completely at random (MCAR)      277
Missing data, modelled as zero      26
Missing data, nonignorable      7 52 60—61 102 103 106 276 277
Missing data, representation      61 271
Missing data, treatment strategies      60 102—110
Modal skewness      280
Mode estimator      279
Modified $R^2$ statistic      134
Monotone missingness      107
Moving-window characterizations      252—263
Nesting      187
Niche      16
Noise      96—97
Noise versus anomalies      33
Nominal variable      264
Nonadditivity model      229
Noninformative variable      25 93—102 276
Noninformative variable, application-specific      95
Noninformative variable, external      94
Noninformative variable, inherent      94
Nonlinear digital filters      117 202
Nonsampling errors      52
Norm      179
NULLs      61—63
Occam’s hatchet      98
Occam’s razor      98
Order statistics definition      5
Ordinal variable      264
Outlier model, additive      70 115
Outlier model, contaminated normal      72 79
Outlier model, discrete mixture      71
Outlier model, point contamination      71
Outlier model, Poisson      112
Outlier model, replacement      71
Outlier model, slippage      73
Outlier model, univariate      70—73
Outlier, common mode      53 237 277
Outlier, definition      2 23
Outlier, detection      23 69—91
Outlier, good      13—16
Outlier, isolated      112 277
Outlier, lower      36
Outlier, multivariate      3 9 24 37—40 124—138
Outlier, orientation      39
Outlier, patchy      112 119 277
Outlier, sources      52—53
Outlier, time-series      40 110—124
Outlier, univariate      2 3 9 11 23—24 34—37 69—91
Outlier, upper      36
Outlier-sensitivity, Galton’s skewness measure      44
Outlier-sensitivity, interquartile distance (IQD)      43
Outlier-sensitivity, kurtosis      18
Outlier-sensitivity, Mann — Whitney test      46
Outlier-sensitivity, mean      20 41—43
Outlier-sensitivity, median      43
Outlier-sensitivity, skewness      43
Outlier-sensitivity, t-test      45
Outlier-sensitivity, variance      43
Oversampling      231
Permutation invariance      151 154 157
Platykurtic      232
Plug flow model      242
Poisson’s ratio      13
Preface      ix
Princeton Robustness Study      20 73 152 194 275 278
Principal component regression (PCR)      130
Principal components analysis (PCA)      130 205
Pseudonorm      158
Pyramids      228
Quantile-quantile (Q-Q) plot      194—195 219 220 235
Random subsets      26 208—212 238
Random subsets, disjoint      208—209
Random subsets, limitations      270
Ranking populations      228
Regression, comparisons      272
Regression, depth-based      137—138
Regression, iteratively reweighted least squares      201
Regression, least median of squares (LMS)      22 275
Regression, M-estimators      274
Regression, ordinary least squares (OLS)      98—102 188—189 215 274
Regression, set-theoretic      169
Relational database      57 95
Resistant      25
Root Mean Square (RMS)      120
Root sequence      115 118—120
Sampling bias      60
Sampling, Bernoulli      224
Sampling, cluster      230
Sampling, importance      211
Sampling, model-based      223
Sampling, Poisson      225
Sampling, probability proportional-to-size      225
Sampling, random with replacement      208
Sampling, random without replacement      208
Sampling, scheme      26 178 190—191
Sampling, sequential      230
Sampling, stratified      225
Sampling, subset-based      178 207—268
Sampling, systematic      223
Scenario      25 178 180—186
Set-valued variables      108 166—172
Shift errors      283
Silhouette coefficient      97 192
1 2
blank
Ðåêëàìà
blank
blank
HR
@Mail.ru
       © Ýëåêòðîííàÿ áèáëèîòåêà ïîïå÷èòåëüñêîãî ñîâåòà ìåõìàòà ÌÃÓ, 2004-2024
Ýëåêòðîííàÿ áèáëèîòåêà ìåõìàòà ÌÃÓ | Valid HTML 4.01! | Valid CSS! Î ïðîåêòå