chapter 10 solution lecture notes

Lecture Notes: Introduction to Finite Element Method (Chapter 2)

Lecture Notes: Introduction to Finite Element Method (Chapter 2)
... Lecture Notes: Introduction to Finite Element Method Chapter Introduction Row and Column Vectors v = [1 v v2 v3 ]  w1    w = w  w   3 ... system of equations is to found the inverse of the coefficient matrix © 1998 Yijun Liu, University of Cincinnati 11 Lecture Notes: Introduction to Finite Element Method Chapter Introduction Solution ... det   = ad − bc c d  © 1998 Yijun Liu, University of Cincinnati Lecture Notes: Introduction to Finite Element Method Chapter Introduction and a11 a12 det a21 a22   a31 a32 a13  a23  =...
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Data Mining: Introduction Lecture Notes for Chapter 1 Introduction to Data Mining ppt

Data Mining: Introduction Lecture Notes for Chapter 1 Introduction to Data Mining ppt
... better, customized services for an edge (e.g in Customer Relationship Management) © Tan,Steinbach, Kumar Introduction to Data Mining Why Mine Data? Scientific Viewpoint Data collected and stored at ... the data is never analyzed at all 4,000,000 3,500,000 The Data Gap 3,000,000 2,500,000 2,000,000 1, 500,000 Total new disk (TB) since 19 95 1, 000,000 Number of analysts 500,000 19 95 19 96 19 97 19 98 ... 19 96 19 97 19 98 19 99 © Tan,Steinbach, KumarKamath, V Kumar, Data Mining for Mining and Engineering Applications” From: R Grossman, C Introduction to Data Scientific What is Data Mining? Many Definitions...
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Data Mining: Data Lecture Notes for Chapter 2 Introduction to Data Mining potx

Data Mining: Data Lecture Notes for Chapter 2 Introduction to Data Mining potx
... p2 p3 p4 p1 p3 p4 p2 0 y 1 p1 p1 p2 p3 p4 x 2. 828 3.1 62 5.099 p2 2. 828 1.414 3.1 62 p3 3.1 62 1.414 p4 5.099 3.1 62 Distance Matrix © Tan,Steinbach, Kumar Introduction to Data Mining 50 Minkowski ... Tan,Steinbach, Kumar Introduction to Data Mining 42 Mapping Data to a New Space Fourier transform Wavelet transform Two Sine Waves © Tan,Steinbach, Kumar Two Sine Waves + Noise Introduction to Data Mining ... Tan,Steinbach, Kumar Introduction to Data Mining 19 Ordered Data Spatio-Temporal Data Average Monthly Temperature of land and ocean © Tan,Steinbach, Kumar Introduction to Data Mining 20 Data Quality...
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Data Mining: Exploring Data Lecture Notes for Chapter 3 Introduction to Data Mining potx

Data Mining: Exploring Data Lecture Notes for Chapter 3 Introduction to Data Mining potx
... object Introduction to Data Mining separate face becomes a Star Plots for Iris Data Setosa Versicolour Virginica © Tan,Steinbach, Kumar Introduction to Data Mining 29 Chernoff Faces for Iris Data ... Tan,Steinbach, Kumar Introduction to Data Mining 35 OLAP Operations: Data Cube The key operation of a OLAP is the formation of a data cube A data cube is a multidimensional representation of data, together ... Kumar Introduction to Data Mining 11 Representation Is the mapping of information to a visual format Data objects, their attributes, and the relationships among data objects are translated into...
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Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining pptx

Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining pptx
... same data! 10 © Tan,Steinbach, Kumar Introduction to Data Mining Decision Tree Classification Task Decision Tree © Tan,Steinbach, Kumar Introduction to Data Mining Apply Model to Test Data Test Data ... P(C2) = 4/ 6 Error = – max (2/6, 4/ 6) = – 4/ 6 = 1/3 Introduction to Data Mining 43 Comparison among Splitting Criteria For a 2-class problem: © Tan,Steinbach, Kumar Introduction to Data Mining 44 Misclassification ... > Yes 3 3 2 3 3 No 4 4 Gini © Tan,Steinbach, Kumar 0 .42 0 0 .40 0 0.375 0. 343 0 .41 7 Introduction to Data Mining 0 .40 0 0.300 0. 343 0.375 0 .40 0 0 .42 0 37 Alternative Splitting Criteria...
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Data Mining Classification: Alternative Techniques - Lecture Notes for Chapter 5 Introduction to Data Mining pdf

Data Mining Classification: Alternative Techniques - Lecture Notes for Chapter 5 Introduction to Data Mining pdf
... (3) until stopping criterion is met © Tan,Steinbach, Kumar Introduction to Data Mining 14 Example of Sequential Covering (ii) Step © Tan,Steinbach, Kumar Introduction to Data Mining 15 Example ... Tan,Steinbach, Kumar Introduction to Data Mining 27 Indirect Methods © Tan,Steinbach, Kumar Introduction to Data Mining 28 Indirect Method: C4.5rules Extract rules from an unpruned decision tree For each ... have the k smallest distance to x © Tan,Steinbach, Kumar Introduction to Data Mining 39 nearest-neighbor Voronoi Diagram © Tan,Steinbach, Kumar Introduction to Data Mining 40 Nearest Neighbor Classification...
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Data Mining Association Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 6 Introduction to Data Mining pdf

Data Mining Association Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 6 Introduction to Data Mining pdf
... 159 3 56 357 68 9 Introduction to Data Mining 367 368 22 Subset Operation Using Hash Tree Hash Function transaction 1+ 23 56 2+ 3 56 12+ 3 56 1,4,7 3+ 56 3 ,6, 9 2,5,8 13+ 56 234 567 15+ 145 1 36 345 ... 159 Introduction to Data Mining 3 56 357 68 9 367 368 23 Subset Operation Using Hash Tree Hash Function transaction 1+ 23 56 2+ 3 56 12+ 3 56 1,4,7 3+ 56 3 ,6, 9 2,5,8 13+ 56 234 567 15+ 145 1 36 345 ... 3 ,6, 9 1,4,7 234 567 345 1 36 145 2,5,8 124 457 © Tan,Steinbach, Kumar 125 458 Introduction to Data Mining 159 3 56 357 68 9 367 368 17 Association Rule Discovery: Hash tree Hash Function 1,4,7 Candidate...
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Data Mining Association Rules: Advanced Concepts and Algorithms Lecture Notes for Chapter 7 Introduction to Data Mining docx

Data Mining Association Rules: Advanced Concepts and Algorithms Lecture Notes for Chapter 7 Introduction to Data Mining docx
... viable candidate, then it can be obtained by merging w with < {1} {2 6} {5}> © Tan,Steinbach, Kumar Introduction to Data Mining 37 GSP Example © Tan,Steinbach, Kumar Introduction to Data Mining ... 0. 17 = 0.9 Sup(W1, W2, W3) = + + + + 0. 17 = 0. 17 © Tan,Steinbach, Kumar Introduction to Data Mining 20 Multi-level Association Rules Food Electronics Bread Computers Milk Wheat Skim White Foremost ... 7, 8, 1, 1, 1, 8, Introduction to Data Mining 26 Examples of Sequence Data Sequence Database Sequence Element (Transaction) Event (Item) Customer Purchase history of a given customer A set of items...
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Data Mining Cluster Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining pot

Data Mining Cluster Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining pot
... Tan,Steinbach, Kumar Introduction to Data Mining Partitional Clustering Original Points © Tan,Steinbach, Kumar A Partitional Clustering Introduction to Data Mining Hierarchical Clustering p1 p2 Traditional ... e.g., autocorrelation Dimensionality Noise and Outliers Type of Distribution © Tan,Steinbach, Kumar Introduction to Data Mining 18 Clustering Algorithms K-means and its variants Hierarchical clustering ... Kumar K-means Clusters Introduction to Data Mining 44 Overcoming K-means Limitations Original Points © Tan,Steinbach, Kumar K-means Clusters Introduction to Data Mining 45 Hierarchical Clustering...
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Data Mining Cluster Analysis: Advanced Concepts and Algorithms Lecture Notes for Chapter 9 Introduction to Data Mining pot

Data Mining Cluster Analysis: Advanced Concepts and Algorithms Lecture Notes for Chapter 9 Introduction to Data Mining pot
... Density Introduction to Data Mining 33 SNN Clustering Can Handle Differing Densities Original Points © Tan,Steinbach, Kumar SNN Clustering Introduction to Data Mining 34 SNN Clustering Can Handle ... merge (c) and (d) Introduction to Data Mining 13 Chameleon: Clustering Using Dynamic Modeling Adapt to the characteristics of the data set to find the natural clusters Use a dynamic model to measure ... Data Mining 18 Experimental Results: CHAMELEON © Tan,Steinbach, Kumar Introduction to Data Mining 19 Experimental Results: CURE (10 clusters) © Tan,Steinbach, Kumar Introduction to Data Mining...
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The Lecture Notes in Physics Part 10 ppsx

The Lecture Notes in Physics Part 10 ppsx
... of the wavepacket, because it compresses the wavepacket in the x-direction whilst stretching it in the y-direction At the same time, the Bretherton flow induced by the finite wavepacket pushes the ... For instance, Coriolis forces were neglected throughout, but they can be incorporated both in GLM theory and in the other theoretical developments; this has been done in the quoted references The ... couple in an essentially two-dimensional situation (see Fig 5.9) The Bretherton flow belonging to the wavepacket is described by (5 .105 ) In the three-dimensional Boussinesq system the Bretherton...
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lecture operating system chapter 10 case study; UNIX and Linux

lecture operating system chapter 10 case study; UNIX and Linux
... dirent is a directory entry UNIX File System (1) Disk layout in classical UNIX systems UNIX File System (2) Directory entry fields Structure of the i-node UNIX File System (3) The relation between ... managing the terminal UNIX I/O (1) Some of the fields of a typical cdevsw table UNIX I/O (2) The UNIX I/O system in BSD Streams An example of streams in System V The UNIX File System (1) Some important ... directories found in most UNIX systems The UNIX File System (2) • Before linking • After linking (a) Before linking (b) After linking The UNIX File System (3) • Separate file systems • After mounting...
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Chapter 10 lý thuyết mạch 1 Lecture 10 Giới thiệu về biến đổi Laplace

Chapter 10 lý thuyết mạch 1 Lecture 10 Giới thiệu về biến đổi Laplace
...  1 1  ( s  j ) t  ( s  j ) t    e  e   s  j s  j  0 10 Biến đổi laplace  Cosine: L cos( t )    cos( t )e  st dt j t   j t   cos  t   e  e    1 1  ... t )  s Biến đổi Laplace Hàm mũ: L e  at    at  st e e dt      e  ( a  s ) t dt   ( s a )t e sa  0 1  0  sa sa Laplace transform pair: e  at  sa Biến đổi laplace ... ( t )  F2 ( s )  L  f1 ( t )  f ( t )     f1 ( t )  f ( t )  e  st dt      f1 ( t )e dt    f ( t )e  st dt  F1 ( s )  F2 ( s )  st 13 Biến đổi Laplace Dịch chuyển thời...
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Lecture management a pacific rim focus chapter 10 strategic organisation design

Lecture management  a pacific rim focus   chapter 10  strategic organisation design
... structural alternatives CEO, CEO, Alpha Alpha Industries Industries Chief ChiefGeneral General Manager Manager Copier Copierproducts products Chief ChiefGeneral General Manager Manager Photographic ... specialised) area CEO Manager, Manufacturing Manager, Distribution Manager, Administration © 2003 McGraw-Hill Australia Pty Ltd PowerPoint Functional structure Advantages Disadvantages • In-depth expertise ... ChiefGeneral General Manager Manager Industrial Industrial Imaging Imagingproducts products Chief ChiefGeneral General Manager Manager Marine MarineElectronic Electronicproducts products © 2003 McGraw-Hill...
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Lecture Medical assisting: Administrative and clinical procedures with anatomy and physiology (4e) – Chapter 10

Lecture Medical assisting: Administrative and clinical procedures with anatomy and physiology (4e) – Chapter 10
... such as Immunizations Employee health records Medical office financial records • Criteria from IRS financial records AMA, American Hospital Association HIPAA law Federal and state ... Management of patient records Vital to patient care and smooth operation of medical office Paper-based medical records Electronic Health Record (EHR) or Electronic Medical Record (EMR) © 2011 ... reserv ed 10- 12 Ergonomics (cont.) • Tips Place a footstool next to the examination table Take a course in proper lifting Ensure good lighting Wear proper shoes Select storage and shelving...
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