Cluster Analysis and Applications (Record no. 176217)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 07570nam a22006255i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-030-74552-3 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | DE-He213 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240423125258.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 210722s2021 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9783030745523 |
| -- | 978-3-030-74552-3 |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 10.1007/978-3-030-74552-3 |
| Source of number or code | doi |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | Q336 |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | UN |
| Source | bicssc |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | COM021000 |
| Source | bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | UN |
| Source | thema |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 005.7 |
| Edition number | 23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Scitovski, Rudolf. |
| Relator term | author. |
| Relator code | aut |
| -- | http://id.loc.gov/vocabulary/relators/aut |
| 245 10 - TITLE STATEMENT | |
| Title | Cluster Analysis and Applications |
| Medium | [electronic resource] / |
| Statement of responsibility, etc | by Rudolf Scitovski, Kristian Sabo, Francisco Martínez-Álvarez, Šime Ungar. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2021. |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2021. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | X, 271 p. 131 illus., 124 illus. in color. |
| Other physical details | online resource. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 1 -- Introduction. 2 Representatives -- 2.1 Representative of data sets with one feature. 2.1.1. Best LS-representative -- 2.1.2 Best `1-representative -- 2.1.3 Best representative of weighted data -- 2.1.4 Bregman divergences -- 2.2 Representative of data sets with two features -- 2.2.1 Fermat–Torricelli–Weber problem -- 2.2.2 Centroid of a set in the plane -- 2.2.3 Median of a set in the plane -- 2.2.4 Geometric median of a set in the plane -- 2.3 Representative of data sets with several features -- 2.3.1 Representative of weighted data -- 2.4 Representative of periodic data -- 2.4.1 Representative of data on the unit circle -- 2.4.2 Burn diagram -- 3 Data clustering -- 3.1 Optimal k-partition -- 3.1.1 Minimal distance principle and Voronoi diagram -- 3.1.2 k-means algorithm -- 3.2 Clustering data with one feature -- 3.2.1 Application of the LS-distance-like function -- 3.2.2 The dual problem -- 3.2.3 Least absolute deviation principle -- 3.2.4 Clustering weighted data -- 3.3 Clustering data with two or several features -- 3.3.1 Least squares principle -- 3.3.2 The dual problem -- 3.3.3 Least absolute deviation principle -- 3.4 Objective function F(c1, . . . , ck) = Pm i=1 min 1≤j≤k d(cj , ai) -- 4 Searching for an optimal partition -- 4.1 Solving the global optimization problem directly -- 4.2 k-means algorithm II -- 4.2.1 Objective function F using the membership matrix -- 4.2.2 Coordinate Descent Algorithms -- 4.2.3 Standard k-means algorithm -- 4.2.4 k-means algorithm with multiple activations -- 4.3 Incremental algorithm -- 4.4 Hierarchical algorithms -- 4.4.1 Introduction and motivation -- 4.4.2 Applying the Least Squares Principle. 4.5 DBSCAN method -- 4.5.1 Parameters MinPts and 97 4.5.2 DBSCAN algorithm -- 4.5.3 Numerical examples -- 5 Indexes -- 5.1 Choosing a partition with the most appropriate number of clusters -- 5.1.1 Calinski–Harabasz index -- 5.1.2 Davies–Bouldin index -- 5.1.3 Silhouette Width Criterion -- 5.1.4 Dunn index -- 5.2 Comparing two partitions -- 5.2.1 Rand index of two partitions -- 5.2.2 Application of the Hausdorff distance -- 6 Mahalanobis data clustering -- 6.1 Total least squares line in the plane. 6.2 Mahalanobis distance-like function in the plane -- 6.3 Mahalanobis distance induced by a set in the plane -- 6.3.1 Mahalanobis distance induced by a set of points in R n -- 6.4 Methods to search for optimal partition with ellipsoidal clusters -- 6.4.1 Mahalanobis k-means algorithm 139 CONTENTS v -- 6.4.2 Mahalanobis incremental algorithm -- 6.4.3 Expectation Maximization algorithm for Gaussian mixtures -- 6.4.4 Expectation Maximization algorithm for normalized Gaussian mixtures and Mahalanobis k-means algorithm -- 6.5 Choosing partition with the most appropriate number of ellipsoidal clusters -- 7 Fuzzy clustering problem -- 7.1 Determining membership functions and centers -- 7.1.1 Membership functions. 7.1.2 Centers -- 7.2 Searching for an optimal fuzzy partition with spherical clusters -- 7.2.1 Fuzzy c-means algorithm -- 7.2.2 Fuzzy incremental clustering algorithm (FInc) -- 7.2.3 Choosing the most appropriate number of clusters -- 7.3 Methods to search for an optimal fuzzy partition with ellipsoidal clusters -- 7.3.1 Gustafson–Kessel c-means algorithm -- 7.3.2 Mahalanobis fuzzy incremental algorithm (MFInc) -- 7.3.3 Choosing the most appropriate number of clusters -- 7.4 Fuzzy variant of the Rand index -- 7.4.1 Applications -- 8 Applications -- 8.1 Multiple geometric objects detection problem and applications -- 8.1.1 Multiple circles detection problem -- 8.1.2 Multiple ellipses detection problem -- 8.1.3 Multiple generalized circles detection problem -- 8.1.4 Multiple lines detection problem -- 8.1.5 Solving MGOD-problem by using the RANSAC method -- 8.2 Determining seismic zones in an area -- 8.2.1 Searching for seismic zones -- 8.2.2 The absolute time of an event -- 8.2.3 The analysis of earthquakes in one zone -- 8.2.4 The wider area of the Iberian Peninsula -- 8.2.5 The wider area of the Republic of Croatia -- 8.3 Temperature fluctuations -- 8.3.1 Identifying temperature seasons -- 8.4 Mathematics and politics: How to determine optimal constituencies? -- 8.4.1 Mathematical model and the algorithm -- 8.4.2 Defining constituencies in the Republic of Croatia -- 8.4.3 Optimizing the number of constituencies -- 8.5 Iris -- 8.6 Reproduction of Escherichia coli. 9 Modules and the data sets -- 9.1 Functions -- 9.2 Algorithms -- 9.3 Data generating -- 9.4 Test examples -- 9.5 Data sets -- Bibliography -- Index. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | With the development of Big Data platforms for managing massive amount of data and wide availability of tools for processing these data, the biggest limitation is the lack of trained experts who are qualified to process and interpret the results. This textbook is intended for graduate students and experts using methods of cluster analysis and applications in various fields. With clear explanations of ideas and precise definitions of notions, accompanied by numerous examples and exercises together with Mathematica programs and modules, Cluster Analysis and Applications is meant for students and researchers in various disciplines, working in data analysis or data science. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Artificial intelligence |
| General subdivision | Data processing. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Computer science. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Pattern recognition systems. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Algorithms. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Data Science. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Theory and Algorithms for Application Domains. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Automated Pattern Recognition. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Artificial Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Design and Analysis of Algorithms. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine Learning. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Sabo, Kristian. |
| Relator term | author. |
| Relator code | aut |
| -- | http://id.loc.gov/vocabulary/relators/aut |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Martínez-Álvarez, Francisco. |
| Relator term | author. |
| Relator code | aut |
| -- | http://id.loc.gov/vocabulary/relators/aut |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Ungar, Šime. |
| Relator term | author. |
| Relator code | aut |
| -- | http://id.loc.gov/vocabulary/relators/aut |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | SpringerLink (Online service) |
| 773 0# - HOST ITEM ENTRY | |
| Title | Springer Nature eBook |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Printed edition: |
| International Standard Book Number | 9783030745516 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Printed edition: |
| International Standard Book Number | 9783030745530 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Printed edition: |
| International Standard Book Number | 9783030745547 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://doi.org/10.1007/978-3-030-74552-3">https://doi.org/10.1007/978-3-030-74552-3</a> |
| 912 ## - | |
| -- | ZDB-2-SCS |
| 912 ## - | |
| -- | ZDB-2-SXCS |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks-CSE-Springer |
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