LEADER 02848nam 2200601Ia 450 001 9910454759603321 005 20200520144314.0 010 $a1-281-81874-7 010 $a9786611818746 010 $a0-8261-9312-9 035 $a(CKB)1000000000705115 035 $a(EBL)423617 035 $a(OCoLC)476263783 035 $a(SSID)ssj0000109979 035 $a(PQKBManifestationID)11124478 035 $a(PQKBTitleCode)TC0000109979 035 $a(PQKBWorkID)10059776 035 $a(PQKB)10535613 035 $a(MiAaPQ)EBC423617 035 $a(Au-PeEL)EBL423617 035 $a(CaPaEBR)ebr10265262 035 $a(EXLCZ)991000000000705115 100 $a19960405d1996 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBehavior and personality$b[electronic resource] $epsychological behaviorism /$fArthur W. Staats 210 $aNew York $cSpringer$dc1996 215 $a1 online resource (465 p.) 225 1 $aSpringer series on behavior therapy and behavioral medicine 300 $aDescription based upon print version of record. 311 $a0-8261-9311-0 320 $aIncludes bibliographical references (p. 388-427) and index. 327 $aContents; Preface; Acknowledgments; 1 Behaviorizing Psychology and Psychologizing Behaviorism: A New Unified Approach; 2 The Basic Learning/Behavior Theory; 3 The Human Learning/Cognitive Theory; 4 The Child Development and Social Interaction Theories; 5 The Theory of Personality: Basic Structure; 6 The Theory of Personality: Content; 7 The Theory of Abnormal Personality and Behavior; 8 Psychological Behavior Therapy/Analysis; 9 A New Type of Theory: Formally and Heuristically; References/Bibliography; Index 330 $aIn this capstone work, Arthur Staats synthesizes more than four decades of research, theory, and study into a new generation of behaviorism that offers insights and future directions for researchers, professionals, and students. Staats's unified theory of psychological behaviorism builds on current theories in child development, personality, psychological measurement, and abnormal behavior. His theoretical model provides new ways to consider human behavior as a whole that will have implications for research, theory, and practice. 410 0$aSpringer series on behavior therapy and behavioral medicine (Unnumbered) 606 $aBehaviorism (Psychology) 606 $aPersonality 608 $aElectronic books. 615 0$aBehaviorism (Psychology) 615 0$aPersonality. 676 $a150.19/43 676 $a155.2 700 $aStaats$b Arthur W$0192255 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910454759603321 996 $aBehavior and personality$92061374 997 $aUNINA LEADER 07121nam 2200457 450 001 996464419303316 005 20220411232549.0 010 $a3-030-74552-X 035 $a(CKB)4100000011984528 035 $a(MiAaPQ)EBC6682763 035 $a(Au-PeEL)EBL6682763 035 $a(OCoLC)1261379859 035 $a(PPN)260304484 035 $a(EXLCZ)994100000011984528 100 $a20220411d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aCluster analysis and applications /$fRudolf Scitovski [and three others] 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (277 pages) 311 $a3-030-74551-1 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- Contents -- 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 I -- 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)=i=1m min1?j?kd(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 ? -- 4.5.2 DBSCAN Algorithm -- Main DBSCAN Algorithm -- 4.5.3 Numerical Examples -- 5 Indexes. 327 $a5.1 Choosing a Partition with the Most Appropriate Numberof 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 Rn -- 6.4 Methods to Search for Optimal Partition with Ellipsoidal Clusters -- 6.4.1 Mahalanobis k-Means Algorithm -- 6.4.2 Mahalanobis Incremental Algorithm -- 6.4.3 Expectation Maximization Algorithm for GaussianMixtures -- 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 The Number of Geometric Objects Is Known in Advance -- 8.1.2 The Number of Geometric Objects Is Not Known in Advance. 327 $a8.1.3 Searching for MAPart and Recognizing GeometricObjects -- 8.1.4 Multiple Circles Detection Problem -- Circle as the Representative of a Data Set -- Artificial Data Set Originating from a Single Circle -- The Best Representative -- Multiple Circles Detection Problem in the Plane -- The Number of Circles Is Known -- KCC Algorithm -- The Number of Circles Is Not Known -- Real-World Images -- 8.1.5 Multiple Ellipses Detection Problem -- A Single Ellipse as the Representative of a Data Set -- Artificial Data Set Originating from a Single Ellipse -- The Best Representative -- Multiple Ellipses Detection Problem -- The Number of Ellipses Is Known in Advance -- KCE Algorithm -- The Number of Ellipses Is Not Known in Advance -- Real-World Images -- 8.1.6 Multiple Generalized Circles Detection Problem -- Real-World Images -- 8.1.7 Multiple Lines Detection Problem -- A Line as Representative of a Data Set -- The Best TLS-Line in Hesse Normal Form -- The Best Representative -- Multiple Lines Detection Problem in the Plane -- The Number of Lines Is Known in Advance -- KCL Algorithm -- The Number of Lines Is Not Known in Advance -- Real-World Images -- 8.1.8 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? -- -- Defining the Problem -- 8.4.1 Mathematical Model and the Algorithm -- Integer Approach -- Linear Relaxation Approach -- 8.4.2 Defining Constituencies in the Republic of Croatia. 327 $aApplying the Linear Relaxation Approach to the Model with 10 Constituencies -- Applying the Integer Approach to the Model with 10 Constituencies -- 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. 606 $aCluster analysis 615 0$aCluster analysis. 676 $a519.53 700 $aScitovski$b Rudolf$0846245 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996464419303316 996 $aCluster analysis and applications$92833847 997 $aUNISA