توضیحاتی در مورد کتاب Advances in Data Science and Classification: Proceedings of the 6th Conference of the International Federation of Classification Societies (IFCS-98) Università “La Sapienza”, Rome, 21–24 July, 1998
نام کتاب : Advances in Data Science and Classification: Proceedings of the 6th Conference of the International Federation of Classification Societies (IFCS-98) Università “La Sapienza”, Rome, 21–24 July, 1998
ویرایش : Softcover reprint of the original 1st ed. 1998
عنوان ترجمه شده به فارسی : پیشرفتها در علم داده و طبقهبندی: مجموعه مقالات ششمین کنفرانس فدراسیون بینالمللی انجمنهای طبقهبندی (IFCS-98) دانشگاه "La Sapienza"، رم، 21 تا 24 ژوئیه، 1998
سری : Studies in Classification, Data Analysis, and Knowledge Organization
نویسندگان : J. Douglas Carroll, Anil Chaturvedi (auth.), Professor Alfredo Rizzi, Professor Maurizio Vichi, Professor Dr. Hans-Hermann Bock (eds.)
ناشر : Springer Berlin Heidelberg
سال نشر : 1998
تعداد صفحات : 677
ISBN (شابک) : 9783540646419 , 9783642722530
زبان کتاب : English
فرمت کتاب : pdf
حجم کتاب : 44 مگابایت
بعد از تکمیل فرایند پرداخت لینک دانلود کتاب ارائه خواهد شد. درصورت ثبت نام و ورود به حساب کاربری خود قادر خواهید بود لیست کتاب های خریداری شده را مشاهده فرمایید.
توضیحاتی در مورد کتاب :
این کتاب پیشرفتهای جدیدی را در طبقهبندی و تجزیه و تحلیل دادهها ارائه میکند و موضوعات جدیدی را ارائه میکند که مورد توجه اصلی آمار مدرن هستند. به طور خاص، اینها شامل نظریه طبقه بندی، تجزیه و تحلیل داده های چند متغیره، داده های چند طرفه، تجزیه و تحلیل ساختار مجاورت، نرم افزار جدید برای طبقه بندی و تجزیه و تحلیل داده ها، و برنامه های کاربردی در علوم اجتماعی، اقتصادی، پزشکی و سایر علوم است. برای بسیاری از این موضوعات، این کتاب یک وضعیت سیستماتیک از هنر نوشته شده توسط محققان برتر جهان را ارائه می دهد. این کتاب به عنوان مقدمه ای مفید برای حوزه طبقه بندی و تجزیه و تحلیل داده ها برای پژوهشگران عمل می کند و از انتقال پیشرفت های جدید در علم داده و طبقه بندی به طیف گسترده ای از کاربردها پشتیبانی می کند.
فهرست مطالب :
Content:
Front Matter....Pages I-XV
Front Matter....Pages 1-1
K-midranges clustering....Pages 3-14
A New Method for Detecting Influential Observations in Nonhierarchical Cluster Analysis....Pages 15-20
Non-probabilistic Classification....Pages 21-28
An investigation of nine procedures for detecting the structure in a data set....Pages 29-36
Clustering and Outlier Identification: Fixed Point Cluster Analysis....Pages 37-42
Clustering Large Datasets of Mixed Units....Pages 43-48
A Critical Approach to Non-Parametric Classification of Compositional Data....Pages 49-56
Clustering Based on Wavelet Transform: Applications to Point Pattern Clustering and to High-Dimensional Data Analysis....Pages 57-64
Beyond Simpson’s Paradox: One Problem in Data Science....Pages 65-72
For Consensus (With Branch Lengths)....Pages 73-80
Consensus of classifications: the case of trees....Pages 81-90
The Information Content of Consensus Trees....Pages 91-98
Compatible and Complementary Fuzzy Partitions....Pages 99-104
Two Methods of Fuzzy c-Means and Classification Functions....Pages 105-110
Dynamic Determination of Mixing Parameters in Fuzzy Clustering....Pages 111-116
A Dynamic Additive Fuzzy Clustering Model....Pages 117-124
Application of fuzzy mathematical morphology for pattern classification....Pages 125-130
Statistical Conditions for a Linear Complexity for an Algorithm of Hierarchical Classification Under Constraint of Contiguity....Pages 131-136
Constrained Clustering Problems....Pages 137-144
A constrained clusterwise procedure for segmentation....Pages 145-151
Front Matter....Pages 1-1
Maximal predictive clustering with order constraint: a linear and optimal algorithm....Pages 153-160
A linear programming based heuristic for a hard clustering problem on trees....Pages 161-170
Two Principal Points of Symmetric Distributions....Pages 171-176
Pertinence for a Classification....Pages 177-184
Global stochastic optimization techniques applied to partitioning....Pages 185-190
MCMC Inference for Model-based Cluster analysis....Pages 191-196
On the Asymptotic Normality of a Resubstitution Error Estimate....Pages 197-204
Stochastic Methods for Generative Systems Analysis....Pages 205-210
Compensatory Rules for Optimal Classification with Mastery Scores....Pages 211-218
Front Matter....Pages 219-219
About the Automatic Detection of Training Sets for Multispectral Images Classification....Pages 221-226
A “Leaps and Bounds” Algorithm for Variable Selection in Two-Group Discriminant Analysis....Pages 227-232
Canonical Discriminant Analysis of Multinomial Samples with Applications to Textual Data....Pages 233-237
How to extract predictive binary attributes from a categorical one....Pages 239-244
A Density Distance Based Approach to Projection Pursuit Discriminant Analysis....Pages 245-250
Two Group Linear Discrimination Based on Transvariation Measures....Pages 251-256
A new geometrical hypothesis for clustering and discriminant analysis....Pages 257-264
Clustering and Neural Networks....Pages 265-277
Application of Self-Organizing Maps to Outlier Identification and Estimation of Missing Data....Pages 279-286
Neuro-Fuzzy Classification....Pages 287-294
Computational Enhancements in Tree-Growing Methods....Pages 295-302
Front Matter....Pages 219-219
A New Way to Build Oblique Decision Trees Using Linear Programming....Pages 303-309
Classification and Regression Trees Software and New Developments....Pages 311-318
STABLE: a visual environment for the study of multivariate data....Pages 319-322
Front Matter....Pages 323-323
Some recent Developments in Factor Analysis and the Search for proper Communalities....Pages 325-334
Bayesian Factor Analysis Model and Choosing the Number of Factors Using a New Informational Complexity Criterion....Pages 335-342
Nonlinear Biplots for Multivariate Normal Grouped Populations....Pages 343-348
Discretization as a tool in cluster analysis....Pages 349-354
A Robust Biplot Representation of Two-way Tables....Pages 355-361
Graphical factor analysis models: specification and model comparison....Pages 363-368
Graphical Analysis of Fully Ranked Data....Pages 369-374
Improper Solutions in Exploratory Factor Analysis: Causes and Treatments....Pages 375-382
Front Matter....Pages 383-383
Symbolic Clustering Of Probabilistic Data....Pages 385-390
Statistical proximity functions of Boolean symbolic objects based on histograms....Pages 391-396
Vertices Principal Components Analysis With an Improved Factorial Representation....Pages 397-402
On the Complexity of Computation with Symbolic Objects using Domain Knowledge....Pages 403-408
Symbolic Data Analysis: a Mathematical Framework and Tool for Data Mining....Pages 409-416
Symbolic Kernel Discriminant Analysis....Pages 417-423
Decision Trees and Uncertain Inference....Pages 425-431
Interpretation and Reduction of Galois Lattices of Complex Data....Pages 433-440
Proximity function for clustering of software objects....Pages 441-448
Front Matter....Pages 383-383
Textual data analysis for open-questions in repeated surveys....Pages 449-456
Three-way textual data analysis....Pages 457-464
Classification Problems in Text Analysis and Information Retrieval....Pages 465-472
Text Mining - Knowledge extraction from unstructured textual data....Pages 473-480
Front Matter....Pages 481-481
A general formulation of multidimensional unfolding models involving the latitude of acceptance....Pages 483-488
The Data Theory Scaling System....Pages 489-496
A Note on Identification and Similarity Models....Pages 497-502
Generalized Distance Measures for Asymmetric Multivariate Distributions....Pages 503-508
Obtaining Disparities as Slopes of Greatest Minorant Convex....Pages 509-516
Non-parametric regression models for the conjoint analysis of qualitative and quantitative data....Pages 517-524
Local multivariate analysis....Pages 525-530
Non-parametric Regression and Non-linear Data Analysis: an overview....Pages 531-538
Simple and Multiple Regression Splines with Bounded Optimal Knots....Pages 539-546
Interpretation of a Cross-Classification: a New Method and an Application....Pages 547-554
Bidimensional Kalman filter and Iterated Conditional Modes....Pages 555-560
An Iterative Reweighting Procedure to Robustify Projection Pursuit Regression....Pages 561-566
Statistical Multivariate Techniques for the Stock Location Assignment Problem....Pages 567-572
A Bayesian Approach to the Analysis of Spatial Time Series....Pages 573-578
Dissimilarities Between Trajectories of a Three-Way Longitudinal Data Set....Pages 579-584
Front Matter....Pages 585-592
An Overview of Three-way Analysis and Some Recent Developments....Pages 481-481
Studying the Diffusion of Three-Mode Analysis in Chemistry: Design Considerations....Pages 593-602
Selection of Classification Models Using Data Envelopment Analysis....Pages 603-611
Tests for the Rank of Matrices of Hilbert-Schmidt Products....Pages 613-618
Application of Three-Way Data Clustering to Analysis of Lymphocyte Subset Numbers in Japanese Hemophiliacs Infected with HIV-1....Pages 619-625
Front Matter....Pages 627-632
On an Error Detecting Algorithm on Textual Data Files....Pages 633-633
Hierarchy Techniques of Multidimensional Data Analysis (MDA) in Social Medicine Research....Pages 635-640
Classification and Decision Making in Forensic Sciences: the Speaker Identification Problem....Pages 641-646
Ordinal Variables in the Segmentation of Advertisement Receivers....Pages 647-654
Some Experimental Trial of Electronic Surveys on the Internet Environments....Pages 655-662
Back Matter....Pages 663-668
....Pages 669-681
توضیحاتی در مورد کتاب به زبان اصلی :
The book provides new developments in classification and data analysis, and presents new topics which are of central interest to modern statistics. In particular, these include classification theory, multivariate data analysis, multi-way data, proximity structure analysis, new software for classification and data analysis, and applications in social, economic, medical and other sciences. For many of these topics, this book provides a systematic state of the art written by top researchers in the world. This book will serve as a helpful introduction to the area of classification and data analysis for research workers and support the transfer of new advances in data science and classification to a wide range of applications.