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xxxv Index 6 Cs 1254, 1258 Apriori-based Graph Mining (AGM) 13 6 Ps 1253 Apriori with Subgroup and Constraint (ASC) 948 Arabidopsis thaliana 205, 227 A ArM32 operations 458 ArnetMiner 331 abnormal alarm 2183 Arterial Pressure (AP) 902 academic theory 1678 ArTex with Resolutions (ARes) 1047 Access Methods (AM) 449, 468 arthrokinetics restrictions 653 across-stage inferencing 568, 575-576, 580, 583 Artificial Intelligence (AI) 991, 1262 Active Learning (AL) 67, 69 Artificial Neural Networks (ANN) 250, 299, 367, 704, ADaMSoft 1315 1114, 1573, 2253 Adaptive Modulation and Coding (AMC) 345 Art of War 477 Adaptive Neuro Fuzzy Inference System (ANFIS) Asset Management (AM) 1607 1114-1115, 1124, 2250, 2252, 2254-2255, 2268 Association Rule Mining (ARM) 28, 47, 108, 148, Additive White Gaussian Noise (AWGN) 2084 684, 856, 974, 989, 1740 advanced search 1314, 1317, 1338 ANGEL data 846-847 Adverse Drug Events (ADEs) 1942 approaches 1739-1740, 1743, 1748-1749 Agglomerative Hierarchical Clustering (AHC) 1435 association rules 57, 65, 587 aggregate constraint 1842 calendric 594 aggregate function 681-682, 686, 1434, 1842, 2028- maximal 503-509, 512-514 2029 quality measures 130 agrometeorological agents 1625 Association rules for Textures (ArTex) 1047 agrometeorological methods 1625-1626 Associative Classification (AC) 975 agrometeorological variables 1626 atomic condition 285-287 ambiguity-directed sampling 71 Attention Deficit Hyperactivity Disorder (ADHD) American Bureau of Shipping (ABS) 2175 1463 American National Election Studies (ANES) 30, 42 Attention, Interest, Desire, and Action (AIDA) 1261 analysis of uses 1170 attitude and conjoint analysis 1170 Analytical Hierarchy Process (AHP) 2153, 2160, 2171 attribute interpretation 520 A New Global Environment for Learning (ANGEL) attribute uncertainty 520-521, 674 838 Attribute Value Frequency (AVF) algorithm 171 Ant Colony Optimization (ACO) 379, 780, 2134, 2144 Automated Driver’s License Examination System Anti-Money Laundering (AML) 2193, 2206 (ADLES) 1780 Application Programming Interface (API) 1375, 1411, Automatic Decision Systems (ADS) 1876 2016 Automatic Repeat Request (ARQ) 353 Apriori algorithm 17, 36, 38, 56-58, 119, 132, 519, Autoregressive Integrated Moving Average models 524, 592-593, 847, 862, 865-866, 956-958, 960, (ARIMA) 1738 975-976, 1016, 1155, 1437, 1742, 1745, 1750, Autoregressive Moving Average models (ARMA) 1941 1738 Apriori association mining 32 Volume I, pp. 1-585; Volume II, pp. 586-1162; Volume III, pp. 1163-1736; Volume IV, pp. 1737-2284 Index Business Performance Management (BPM) 1876 B business social network 231-232, 246, 326, 334 Back-Propagation Algorithm (BPA) 706, 1114 Business Social Network Sites (BSNS) 231 Back-Propagation Network (BPN) 1115 Business-to-Business (B2B) 234 Bag-of-Words (BoW) 1103 Business-to-Customer (B2C) 1449 Balanced Scorecard (BSC) 476 business understanding 2002 bankruptcy risk 920, 922, 924, 927-928, 937, 939- 940, 942, 946 C Basic Information Elements (BIE) 453 Bayesian classifiers 528, 1019-1020, 1028-1029, C4.5 algorithm 32, 36, 780, 840, 845 1039, 1062 C4.

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Correspondence Analysis
NCSS Statistical Software NCSS.com Chapter 430 Correspondence Analysis Introduction Correspondence analysis (CA) is a technique for graphically displaying a two-way table by calculating coordinates representing its rows and columns. These coordinates are analogous to factors in a principal components analysis (used for continuous data), except that they partition the Chi-square value used in testing independence instead of the total variance. For those of you who are new to CA, we suggest that you obtain Greenacre (1993). This is an excellent introduction to the subject, is very readable, and is suitable for self-study. If you want to understand the technique in detail, you should obtain this (paperback) book. Discussion We will explain CA using the following example. Suppose an aptitude survey consisting of eight yes or no questions is given to a group of tenth graders. The instructions on the survey allow the students to answer only those questions that they want to. The results of the survey are tabulated as follows. Aptitude Survey Results – Counts Question Yes No Total Q1 155 938 1093 Q2 19 63 82 Q3 395 542 937 Q4 61 64 125 Q5 1336 876 2212 Q6 22 14 36 Q7 864 354 1218 Q8 920 185 1105 Total 3772 3036 6808 Take a few moments to study this table and see what you can discover. The most obvious pattern is that many of the students did not answer all the questions. This makes response patterns between rows difficult to analyze. To solve this problem of differential response rates, we create a table of row percents (or row profiles as they are called in CA).
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A Correspondence Analysis of Child-Care Students' and Medical
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Research Article the Use of Multiple Correspondence Analysis to Explore Associations Between Categories of Qualitative Variables in Healthy Ageing
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Correspondence Analysis and Classification Gilbert Saporta, Ndeye Niang Keita
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Implementing and Interpreting Canonical Correspondence Analysis in SAS Laxman Hegde, Frostburg State University, Frostburg, MD
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Measuring Explained Variance in Multiple Correspondence Analysis
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Correspondence Analysis
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The Geometric Interpretation of Correspondence Analysis Author(S): Michael Greenacre and Trevor Hastie Source: Journal of the American Statistical Association, Vol
The Geometric Interpretation of Correspondence Analysis Author(s): Michael Greenacre and Trevor Hastie Source: Journal of the American Statistical Association, Vol. 82, No. 398 (Jun., 1987), pp. 437-447 Published by: Taylor & Francis, Ltd. on behalf of the American Statistical Association Stable URL: https://www.jstor.org/stable/2289445 Accessed: 26-01-2020 22:24 UTC JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at https://about.jstor.org/terms Taylor & Francis, Ltd., American Statistical Association are collaborating with JSTOR to digitize, preserve and extend access to Journal of the American Statistical Association This content downloaded from 171.66.12.245 on Sun, 26 Jan 2020 22:24:02 UTC All use subject to https://about.jstor.org/terms The Geometric Interpretation of Correspondence Analysis MICHAEL GREENACRE and TREVOR HASTIE* Correspondence analysis is an exploratory multivariate technique that indicator (or "dummy") variables. The correspondence converts a data matrix into a particular type of graphical display in which analysis of this indicator matrix is known as multiple cor- the rows and columns are depicted as points. The method has a long respondence analysis or homogeneity analysis (Gifi 1981). and varied history and has appeared in different forms in the psycho- metric and ecological literature, among others.
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