國立虎尾科技大學 |

Cluster Analysis and Applications

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Cluster Analysis and Applications/ by Rudolf Scitovski, Kristian Sabo, Francisco Martínez-Álvarez, Šime Ungar.
Author:	Scitovski, Rudolf.
other author:	Sabo, Kristian.
Description:	X, 271 p. 131 illus., 124 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Data structures (Computer science). -
Online resource:	https://doi.org/10.1007/978-3-030-74552-3
ISBN:	9783030745523

Cluster Analysis and Applications
Scitovski, Rudolf.

Cluster Analysis and Applications[electronic resource] /by Rudolf Scitovski, Kristian Sabo, Francisco Martínez-Álvarez, Šime Ungar. - 1st ed. 2021. - X, 271 p. 131 illus., 124 illus. in color.online resource.

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.

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.

ISBN: 9783030745523

Standard No.: 10.1007/978-3-030-74552-3doiSubjects--Topical Terms:

680370
Data structures (Computer science).

LC Class. No.: QA76.9.D35

Dewey Class. No.: 005.73

Cluster Analysis and Applications
LDR:06655nam a22004215i 4500 001 1048722
003 DE-He213
005 20210722173950.0
007 cr nn 008mamaa
008 220103s2021 gw | s |||| 0|eng d
020 $a 9783030745523 $9 978-3-030-74552-3
024 7 $a 10.1007/978-3-030-74552-3 $2 doi
035 $a 978-3-030-74552-3
050 4 $a QA76.9.D35
050 4 $a Q350-390
072 7 $a UMB $2 bicssc
072 7 $a COM031000 $2 bisacsh
072 7 $a UMB $2 thema
072 7 $a GPF $2 thema
082 0 4 $a 005.73 $2 23
100 1 $a Scitovski, Rudolf. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1352685
245 1 0 $a Cluster Analysis and Applications $h [electronic resource] / $c by Rudolf Scitovski, Kristian Sabo, Francisco Martínez-Álvarez, Šime Ungar.
250 $a 1st ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a X, 271 p. 131 illus., 124 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a 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 $a 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 $a Data structures (Computer science). $3 680370
650 0 $a Artificial intelligence. $3 559380
650 0 $a Computers. $3 565115
650 0 $a Pattern recognition. $3 1253525
650 0 $a Algorithms. $3 527865
650 0 $a Machine learning. $3 561253
650 1 4 $a Data Structures and Information Theory. $3 1211601
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Theory of Computation. $3 669322
650 2 4 $a Pattern Recognition. $3 669796
650 2 4 $a Algorithm Analysis and Problem Complexity. $3 593923
650 2 4 $a Machine Learning. $3 1137723
700 1 $a Sabo, Kristian. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1352686
700 1 $a Martínez-Álvarez, Francisco. $e editor. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1268264
700 1 $a Ungar, Šime. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1352687
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030745516
776 0 8 $i Printed edition: $z 9783030745530
776 0 8 $i Printed edition: $z 9783030745547
856 4 0 $u https://doi.org/10.1007/978-3-030-74552-3
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)