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 LEADER 03363nam 22005055 450 001 9910255279303321 005 20200706113629.0 010 $a3-319-65572-8 024 7 $a10.1007/978-3-319-65572-7 035 $a(CKB)4100000000587812 035 $a(DE-He213)978-3-319-65572-7 035 $a(MiAaPQ)EBC5045146 035 $a(PPN)222239069 035 $a(EXLCZ)994100000000587812 100 $a20170912d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIntroduction to Turkish Labour Law /$fby Tankut Centel 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XXXVI, 394 p.) 311 $a3-319-65571-X 320 $aIncludes bibliographical references. 327 $aPart I Introduction: Historical Background -- Legal Sources -- Personnel and Agencies -- Basic Concepts -- Area of Implementation.- Part II - The Individual Labour Law: Employment Relationship: The Notion of 'Employment Contract' -- Establishment of the Employment Contract -- Obligations of the Parties Throughout the Employment Contract -- Organization of Work -- The Ending of the Employment Contract.- Part III - The Collective Labour Law: Unions:Union Freedom and Protection -- Organization of Unions -- Membership and Union Activities.- Part IV - The Collective Labour Law: Collective Bargaining: Concluding of Collective Labour Agreement -- Duration and Termination of Collective Labour Agreement -- Amicable Ways of Settlement in Collective Labour Disputes.- Part V - The Collective Labour Law: Strikes and Lockouts: Bans and Restrictions on Strikes and Lockouts -- Strikes -- Lockouts. 330 $aThis book provides essential information on the legal rights of employers and employees in Turkey, plus up-to-date sections on wages, working hours, employment contracts, discrimination laws, and unions.  The work mainly consists of three parts: introduction, individual labour law, and collective labour law in Turkey. The extensive material and numerous court decisions presented in each chapter will introduce readers to the major current debates in labour law and encourage them to engage in critical and independent assessment. As such, the book offers an engaging and accessible overview of the development and status quo of labour law and industrial relations issues in Turkey. . 606 $aPrivate international law 606 $aConflict of laws 606 $aLabor law 606 $aPrivate International Law, International & Foreign Law, Comparative Law $3https://scigraph.springernature.com/ontologies/product-market-codes/R14002 606 $aLabour Law/Social Law$3https://scigraph.springernature.com/ontologies/product-market-codes/R12018 615 0$aPrivate international law. 615 0$aConflict of laws. 615 0$aLabor law. 615 14$aPrivate International Law, International & Foreign Law, Comparative Law . 615 24$aLabour Law/Social Law. 676 $a340.9 676 $a340.2 700 $aCentel$b Tankut$4aut$4http://id.loc.gov/vocabulary/relators/aut$0787337 906 $aBOOK 912 $a9910255279303321 996 $aIntroduction to Turkish Labour Law$91938787 997 $aUNINA