Building Roads. Page 2. I = {;, a, b, c, d, e, ab, ac, ad, ae, bc, bd, be, cd, ce, de, abd, abe, acd, ace, bcd, bce, bde}

Size: px
Start display at page:

Download "Building Roads. Page 2. I = {;, a, b, c, d, e, ab, ac, ad, ae, bc, bd, be, cd, ce, de, abd, abe, acd, ace, bcd, bce, bde}"

Transcription

1 Page

2 Building Roads Page I = {;, a, b, c, d, e, ab, ac, ad, ae, bc, bd, be, cd, ce, de, abd, abe, acd, ace, bcd, bce, bde}

3 Building Roads Page 3 2 a d 3 c b e I = {;, a, b, c, d, e, ab, ac, ad, ae, bc, bd, be, cd, ce, de, abd, abe, acd, ace, bcd, bce, bde} 4

4 Building Roads Page 4 2 a d 3 c b e I = {;, a, b, c, d, e, ab, ac, ad, ae, bc, bd, be, cd, ce, de, abd, abe, acd, ace, bcd, bce, bde} 4

5 Assigning Jobs Page 5 a W b c d e Loader Rock Truck Excavator I = {;, a, b, c, d, e, ab, ac, ad, ae, bc, bd, be, cd, ce, de, abd, abe, acd, ace, bcd, bce, bde}

6 Assigning Jobs Page 6 a W b c d e Loader Rock Truck Excavator I = {;, a, b, c, d, e, ab, ac, ad, ae, bc, bd, be, cd, ce, de, abd, abe, acd, ace, bcd, bce, bde}

7 Assigning Jobs Page 7 a W b c d e Loader Rock Truck Excavator I = {;, a, b, c, d, e, ab, ac, ad, ae, bc, bd, be, cd, ce, de, abd, abe, acd, ace, bcd, bce, bde}

8 Vectors ~a A ~ b A ~c A ~ d A ~e A Page 8

9 Vectors Page 9 ~a A ~ b A ~e ~ d ~c A ~ d A ~e A ~a ~ b ~c

10 ~a A ~ b A ~c A ~ d A ~e A I = {;, a, b, c, d, e, ab, ac, ad, ae, bc, bd, be, cd, ce, de, abd, abe, acd, ace, bcd, bce, bde} ~a ~e Vectors ~ b ~ d ~c Page

11 ~a A ~ b A ~c A ~ d A ~e A I = {;, a, b, c, d, e, ab, ac, ad, ae, bc, bd, be, cd, ce, de, abd, abe, acd, ace, bcd, bce, bde} ~a ~e Vectors ~ b ~ d ~c Page

12 ~a A ~ b A ~c A ~ d A ~e A I = {;, a, b, c, d, e, ab, ac, ad, ae, bc, bd, be, cd, ce, de, abd, abe, acd, ace, bcd, bce, bde} ~a ~e Vectors ~ b ~ d ~c Page 2

13 ~a A ~ b A ~c A ~ d A ~e A I = {;, a, b, c, d, e, ab, ac, ad, ae, bc, bd, be, cd, ce, de, abd, abe, acd, ace, bcd, bce, bde} ~a ~e Vectors ~ b ~ d ~c Page 3

14 ~a A ~ b A ~c A ~ d A ~e A I = {;, a, b, c, d, e, ab, ac, ad, ae, bc, bd, be, cd, ce, de, abd, abe, acd, ace, bcd, bce, bde} a e Vectors b d c Page 4

15 Common Structure Page 5 What was a common structure among the three scenarios?

16 Common Structure Page 6 What was a common structure among the three scenarios? Building Roads, Assigning Jobs, and Vectors all had the same feasible sets":

17 Common Structure Page 7 What was a common structure among the three scenarios? Building Roads, Assigning Jobs, and Vectors all had the same feasible sets": I = {;, a, b, c, d, e, ab, ac, ad, ae, bc, bd, be, cd, ce, de, abd, abe, acd, ace, bcd, bce, bde}

18 Common Structure Page 8 What was a common structure among the three scenarios? Building Roads, Assigning Jobs, and Vectors all had the same feasible sets": I = {;, a, b, c, d, e, ab, ac, ad, ae, bc, bd, be, cd, ce, de, abd, abe, acd, ace, bcd, bce, bde} Notice:

19 Common Structure Page 9 What was a common structure among the three scenarios? Building Roads, Assigning Jobs, and Vectors all had the same feasible sets": I = {;, a, b, c, d, e, ab, ac, ad, ae, bc, bd, be, cd, ce, de, abd, abe, acd, ace, bcd, bce, bde} Notice: (I) ;2I;

20 Common Structure Page 2 What was a common structure among the three scenarios? Building Roads, Assigning Jobs, and Vectors all had the same feasible sets": I = {;, a, b, c, d, e, ab, ac, ad, ae, bc, bd, be, cd, ce, de, abd, abe, acd, ace, bcd, bce, bde} Notice: (I) ;2I; (I2) If I 2Iand J I, then J 2I;

21 Common Structure Page 2 What was a common structure among the three scenarios? Building Roads, Assigning Jobs, and Vectors all had the same feasible sets": I = {;, a, b, c, d, e, ab, ac, ad, ae, bc, bd, be, cd, ce, de, abd, abe, acd, ace, bcd, bce, bde} Notice: (I) ;2I; (I2) If I 2Iand J I, then J 2I; (I3) If I, J 2Iand I > J, then there exists x 2 I such that J [{x} 2I.

22 Matroid Page 22 Definition Let E be a finite set and let I be a collection of subsets of E such that I satisfies properties (I), (I2), and (I3). Then the pair (E, I) is a matroid M. A subset of E that is in I is called an independent set in M. A set that is not independent is called dependent.

23 Matroid Definition Let E be a finite set and let I be a collection of subsets of E such that I satisfies properties (I), (I2), and (I3). Then the pair (E, I) is a matroid M. A subset of E that is in I is called an independent set in M. A set that is not independent is called dependent. Examples: Page 23

24 Matroid Definition Let E be a finite set and let I be a collection of subsets of E such that I satisfies properties (I), (I2), and (I3). Then the pair (E, I) is a matroid M. A subset of E that is in I is called an independent set in M. A set that is not independent is called dependent. Examples: Page 24 A graph, where E is the set of edges and I consists of acyclic subsets of edges. A matroid that can be depicted in this way is called a graphic matroid.

25 Matroid Definition Let E be a finite set and let I be a collection of subsets of E such that I satisfies properties (I), (I2), and (I3). Then the pair (E, I) is a matroid M. A subset of E that is in I is called an independent set in M. A set that is not independent is called dependent. Examples: Page 25 A graph, where E is the set of edges and I consists of acyclic subsets of edges. A matroid that can be depicted in this way is called a graphic matroid. A bipartite graph on vertices (X, Y ), where E = X and I consists of subsets of X that can appear together in a matching. A matroid that can be depicted in this way is called a transversal matroid.

26 Matroid Definition Let E be a finite set and let I be a collection of subsets of E such that I satisfies properties (I), (I2), and (I3). Then the pair (E, I) is a matroid M. A subset of E that is in I is called an independent set in M. A set that is not independent is called dependent. Examples: Page 26 A graph, where E is the set of edges and I consists of acyclic subsets of edges. A matroid that can be depicted in this way is called a graphic matroid. A bipartite graph on vertices (X, Y ), where E = X and I consists of subsets of X that can appear together in a matching. A matroid that can be depicted in this way is called a transversal matroid. A finite set E of vectors from a vector space, where I consists of subsets of E that are linearly independent. A matroid that can be depicted in this way is called a representable matroid.

27 Page 27

28 Page 28

Apriori Algorithm. 1 Bread, Milk 2 Bread, Diaper, Beer, Eggs 3 Milk, Diaper, Beer, Coke 4 Bread, Milk, Diaper, Beer 5 Bread, Milk, Diaper, Coke

Apriori Algorithm. 1 Bread, Milk 2 Bread, Diaper, Beer, Eggs 3 Milk, Diaper, Beer, Coke 4 Bread, Milk, Diaper, Beer 5 Bread, Milk, Diaper, Coke Apriori Algorithm For a given set of transactions, the main aim of Association Rule Mining is to find rules that will predict the occurrence of an item based on the occurrences of the other items in the

More information

DATA MINING II - 1DL460

DATA MINING II - 1DL460 DATA MINING II - 1DL460 Spring 2013 " An second class in data mining http://www.it.uu.se/edu/course/homepage/infoutv2/vt13 Kjell Orsborn Uppsala Database Laboratory Department of Information Technology,

More information

1.4 Euler Diagram Layout Techniques

1.4 Euler Diagram Layout Techniques 1.4 Euler Diagram Layout Techniques Euler Diagram Layout Techniques: Overview Dual graph based methods Inductive methods Drawing with circles Including software demos. How is the drawing problem stated?

More information

Name Date Class. Vertical angles are opposite angles formed by the intersection of two lines. Vertical angles are congruent.

Name Date Class. Vertical angles are opposite angles formed by the intersection of two lines. Vertical angles are congruent. SKILL 43 Angle Relationships Example 1 Adjacent angles are pairs of angles that share a common vertex and a common side. Vertical angles are opposite angles formed by the intersection of two lines. Vertical

More information

Data Mining Techniques

Data Mining Techniques Data Mining Techniques CS 6220 - Section 3 - Fall 2016 Lecture 16: Association Rules Jan-Willem van de Meent (credit: Yijun Zhao, Yi Wang, Tan et al., Leskovec et al.) Apriori: Summary All items Count

More information

Frequent Pattern Mining. Based on: Introduction to Data Mining by Tan, Steinbach, Kumar

Frequent Pattern Mining. Based on: Introduction to Data Mining by Tan, Steinbach, Kumar Frequent Pattern Mining Based on: Introduction to Data Mining by Tan, Steinbach, Kumar Item sets A New Type of Data Some notation: All possible items: Database: T is a bag of transactions Transaction transaction

More information

Mining Association Rules in Large Databases

Mining Association Rules in Large Databases Mining Association Rules in Large Databases Association rules Given a set of transactions D, find rules that will predict the occurrence of an item (or a set of items) based on the occurrences of other

More information

Mining Frequent Patterns without Candidate Generation

Mining Frequent Patterns without Candidate Generation Mining Frequent Patterns without Candidate Generation Outline of the Presentation Outline Frequent Pattern Mining: Problem statement and an example Review of Apriori like Approaches FP Growth: Overview

More information

BCB 713 Module Spring 2011

BCB 713 Module Spring 2011 Association Rule Mining COMP 790-90 Seminar BCB 713 Module Spring 2011 The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Outline What is association rule mining? Methods for association rule mining Extensions

More information

BCNF. Yufei Tao. Department of Computer Science and Engineering Chinese University of Hong Kong BCNF

BCNF. Yufei Tao. Department of Computer Science and Engineering Chinese University of Hong Kong BCNF Yufei Tao Department of Computer Science and Engineering Chinese University of Hong Kong Recall A primary goal of database design is to decide what tables to create. Usually, there are two principles:

More information

Calculus With Analytic Geometry by SM. Yusaf & Prof.Muhammad Amin CHAPTER # 08 ANALYTIC GEOMETRY OF THREE DIMENSONS. Exercise #8.1

Calculus With Analytic Geometry by SM. Yusaf & Prof.Muhammad Amin CHAPTER # 08 ANALYTIC GEOMETRY OF THREE DIMENSONS. Exercise #8.1 CHAPTER # 08 ANALYTIC GEOMETRY OF THREE DIMENSONS Exercise #8.1 Show that the three given points are the vertices of a right triangle, or the vertices of an isosceles triangle, or both. Q#: A 1, 5, 0,

More information

Oracle 1Z0-200 Exam Questions & Answers

Oracle 1Z0-200 Exam Questions & Answers Oracle 1Z0-200 Exam Questions & Answers Number: 1Z0-200 Passing Score: 800 Time Limit: 120 min File Version: 33.2 http://www.gratisexam.com/ Oracle 1Z0-200 Exam Questions & Answers Exam Name: Oracle 11i.E-Business

More information

1 Triangle ABC is graphed on the set of axes below. 3 As shown in the diagram below, EF intersects planes P, Q, and R.

1 Triangle ABC is graphed on the set of axes below. 3 As shown in the diagram below, EF intersects planes P, Q, and R. 1 Triangle ABC is graphed on the set of axes below. 3 As shown in the diagram below, EF intersects planes P, Q, and R. Which transformation produces an image that is similar to, but not congruent to, ABC?

More information

Introduction to Delta-Matroids

Introduction to Delta-Matroids Introduction to Delta-Matroids Carolyn Chun, Iain Moffatt, Steve Noble, Ralf Rueckriemen Brunel University 23/7/2014 Steve Noble ( Brunel University ) Introduction to Delta-Matroids 23/7/2014 1 / 20 Ribbon

More information

Chapter 4: Association analysis:

Chapter 4: Association analysis: Chapter 4: Association analysis: 4.1 Introduction: Many business enterprises accumulate large quantities of data from their day-to-day operations, huge amounts of customer purchase data are collected daily

More information

A B AB CD Objectives:

A B AB CD Objectives: Objectives:. Four variables maps. 2. Simplification using prime implicants. 3. "on t care" conditions. 4. Summary.. Four variables Karnaugh maps Minterms A A m m m3 m2 A B C m4 C A B C m2 m8 C C m5 C m3

More information

Hartmann HONORS Geometry Chapter 3 Formative Assessment * Required

Hartmann HONORS Geometry Chapter 3 Formative Assessment * Required Hartmann HONORS Geometry Chapter 3 Formative Assessment * Required 1. First Name * 2. Last Name * Vocabulary Match the definition to the vocabulary word. 3. Non coplanar lines that do not intersect. *

More information

Market baskets Frequent itemsets FP growth. Data mining. Frequent itemset Association&decision rule mining. University of Szeged.

Market baskets Frequent itemsets FP growth. Data mining. Frequent itemset Association&decision rule mining. University of Szeged. Frequent itemset Association&decision rule mining University of Szeged What frequent itemsets could be used for? Features/observations frequently co-occurring in some database can gain us useful insights

More information

Effectiveness of Freq Pat Mining

Effectiveness of Freq Pat Mining Effectiveness of Freq Pat Mining Too many patterns! A pattern a 1 a 2 a n contains 2 n -1 subpatterns Understanding many patterns is difficult or even impossible for human users Non-focused mining A manager

More information

Performance and Scalability: Apriori Implementa6on

Performance and Scalability: Apriori Implementa6on Performance and Scalability: Apriori Implementa6on Apriori R. Agrawal and R. Srikant. Fast algorithms for mining associa6on rules. VLDB, 487 499, 1994 Reducing Number of Comparisons Candidate coun6ng:

More information

Switching Circuits & Logic Design

Switching Circuits & Logic Design Switching Circuits & Logic Design Jie-Hong Roland Jiang 江介宏 Department of Electrical Engineering National Taiwan University Fall 23 5 Karnaugh Maps K-map Walks and Gray Codes http://asicdigitaldesign.wordpress.com/28/9/26/k-maps-walks-and-gray-codes/

More information

Larger K-maps. So far we have only discussed 2 and 3-variable K-maps. We can now create a 4-variable map in the

Larger K-maps. So far we have only discussed 2 and 3-variable K-maps. We can now create a 4-variable map in the EET 3 Chapter 3 7/3/2 PAGE - 23 Larger K-maps The -variable K-map So ar we have only discussed 2 and 3-variable K-maps. We can now create a -variable map in the same way that we created the 3-variable

More information

Chapter 7: Frequent Itemsets and Association Rules

Chapter 7: Frequent Itemsets and Association Rules Chapter 7: Frequent Itemsets and Association Rules Information Retrieval & Data Mining Universität des Saarlandes, Saarbrücken Winter Semester 2013/14 VII.1&2 1 Motivational Example Assume you run an on-line

More information

Proving Triangles and Quadrilaterals Satisfy Transformational Definitions

Proving Triangles and Quadrilaterals Satisfy Transformational Definitions Proving Triangles and Quadrilaterals Satisfy Transformational Definitions 1. Definition of Isosceles Triangle: A triangle with one line of symmetry. a. If a triangle has two equal sides, it is isosceles.

More information

Stand Alone SLIM-DMX Interface

Stand Alone SLIM-DMX Interface Stand Alone SLIM-DMX Interface V.1.0.1 Summary Technical features of the interface P.02 General pinout and device's connector P.02 Bottom view of the interface P.03 Top view of the interface P.03 Interfaces

More information

Review (pages )

Review (pages ) Review (pages 124 126) 2.1 1. a) In right CDE, CE is D and CD is adjacent to D. Use the tangent ratio in right CDE. tan D adjacent CE tan D CD 7 tan D 10 D 34.9920 D is approximately 35. b) In right FGH,

More information

How to find a minimum spanning tree

How to find a minimum spanning tree Print How to find a minimum spanning tree Definitions Kruskal s algorithm Spanning tree example Definitions Trees A tree is a connected graph without any cycles. It can also be defined as a connected graph

More information

Regular polytopes Notes for talks given at LSBU, November & December 2014 Tony Forbes

Regular polytopes Notes for talks given at LSBU, November & December 2014 Tony Forbes Regular polytopes Notes for talks given at LSBU, November & December 2014 Tony Forbes Flags A flag is a sequence (f 1, f 0,..., f n ) of faces f i of a polytope f n, each incident with the next, with precisely

More information

your answer in scientific notation. 3.0

your answer in scientific notation. 3.0 Section A Foundation Questions (%) 7 1 1. Evaluate (4.8 ) (0.3 ) ( ) without using a calculator and express your answer in scientific notation. (4.8 ) (0.3 7 ) ( 1 4.8 ) ( )[ 0.3 (3)( 3. 16 3. 17 ) ( 7)

More information

Class 7 Lines and Angles

Class 7 Lines and Angles ID : in-7-lines-and-angles [1] Class 7 Lines and Angles For more such worksheets visit www.edugain.com Answer t he quest ions (1) If AD and BD are bisectors of CAB and CBA respectively, f ind sum of angle

More information

Advance Association Analysis

Advance Association Analysis Advance Association Analysis 1 Minimum Support Threshold 3 Effect of Support Distribution Many real data sets have skewed support distribution Support distribution of a retail data set 4 Effect of Support

More information

OPTIMISING ASSOCIATION RULE ALGORITHMS USING ITEMSET ORDERING

OPTIMISING ASSOCIATION RULE ALGORITHMS USING ITEMSET ORDERING OPTIMISING ASSOCIATION RULE ALGORITHMS USING ITEMSET ORDERING ES200 Peterhouse College, Cambridge Frans Coenen, Paul Leng and Graham Goulbourne The Department of Computer Science The University of Liverpool

More information

2 a. 3 (60 cm) cm cm 4

2 a. 3 (60 cm) cm cm 4 Class IX - NCERT Maths Exercise (1.1) Question 1: A traffic signal board, indicating SCHOOL AHEAD, is an equilateral triangle with side a. Find the area of the signal board, using Heron s formula. If its

More information

CSCI 220: Computer Architecture I Instructor: Pranava K. Jha. Simplification of Boolean Functions using a Karnaugh Map

CSCI 220: Computer Architecture I Instructor: Pranava K. Jha. Simplification of Boolean Functions using a Karnaugh Map CSCI 22: Computer Architecture I Instructor: Pranava K. Jha Simplification of Boolean Functions using a Karnaugh Map Q.. Plot the following Boolean function on a Karnaugh map: f(a, b, c, d) = m(, 2, 4,

More information

CIS-331 Fall 2014 Exam 1 Name: Total of 109 Points Version 1

CIS-331 Fall 2014 Exam 1 Name: Total of 109 Points Version 1 Version 1 1. (24 Points) Show the routing tables for routers A, B, C, and D. Make sure you account for traffic to the Internet. Router A Router B Router C Router D Network Next Hop Next Hop Next Hop Next

More information

Complementary, Supplementary, & Vertical Angles

Complementary, Supplementary, & Vertical Angles Unit 4: Lesson 1: Complementary and Supplementary Angles Date: Complementary, Supplementary, & Vertical Angles Type of Angles Definition/Description Complementary Angles Diagram Supplementary Angles Vertical

More information

ERMO DG: Evolving Region Moving Object Dataset Generator. Authors: B. Aydin, R. Angryk, K. G. Pillai Presented by Micheal Schuh May 21, 2014

ERMO DG: Evolving Region Moving Object Dataset Generator. Authors: B. Aydin, R. Angryk, K. G. Pillai Presented by Micheal Schuh May 21, 2014 ERMO DG: Evolving Region Moving Object Dataset Generator Authors: B. Aydin, R. Angryk, K. G. Pillai Presented by Micheal Schuh May 21, 2014 Introduction Spatiotemporal pattern mining algorithms Motivation

More information

Frequent Pattern Mining

Frequent Pattern Mining Frequent Pattern Mining How Many Words Is a Picture Worth? E. Aiden and J-B Michel: Uncharted. Reverhead Books, 2013 Jian Pei: CMPT 741/459 Frequent Pattern Mining (1) 2 Burnt or Burned? E. Aiden and J-B

More information

On Canonical Forms for Frequent Graph Mining

On Canonical Forms for Frequent Graph Mining n anonical Forms for Frequent Graph Mining hristian Borgelt School of omputer Science tto-von-guericke-university of Magdeburg Universitätsplatz 2, D-39106 Magdeburg, Germany Email: borgelt@iws.cs.uni-magdeburg.de

More information

SCHEMA REFINEMENT AND NORMAL FORMS

SCHEMA REFINEMENT AND NORMAL FORMS 19 SCHEMA REFINEMENT AND NORMAL FORMS Exercise 19.1 Briefly answer the following questions: 1. Define the term functional dependency. 2. Why are some functional dependencies called trivial? 3. Give a set

More information

CIS-331 Fall 2013 Exam 1 Name: Total of 120 Points Version 1

CIS-331 Fall 2013 Exam 1 Name: Total of 120 Points Version 1 Version 1 1. (24 Points) Show the routing tables for routers A, B, C, and D. Make sure you account for traffic to the Internet. NOTE: Router E should only be used for Internet traffic. Router A Router

More information

CHAPTER 8. ITEMSET MINING 226

CHAPTER 8. ITEMSET MINING 226 CHAPTER 8. ITEMSET MINING 226 Chapter 8 Itemset Mining In many applications one is interested in how often two or more objectsofinterest co-occur. For example, consider a popular web site, which logs all

More information

10) the plane in two different ways Plane M or DCA (3 non-collinear points) Use the figure to name each of the following:

10) the plane in two different ways Plane M or DCA (3 non-collinear points) Use the figure to name each of the following: Name: Period Date Pre-AP Geometry Fall 2015 Semester Exam REVIEW *Chapter 1.1 Points Lines Planes Use the figure to name each of the following: 1) three non-collinear points (A, C, B) or (A, C, D) or any

More information

Chapter 6: Association Rules

Chapter 6: Association Rules Chapter 6: Association Rules Association rule mining Proposed by Agrawal et al in 1993. It is an important data mining model. Transaction data (no time-dependent) Assume all data are categorical. No good

More information

Association Rules. A. Bellaachia Page: 1

Association Rules. A. Bellaachia Page: 1 Association Rules 1. Objectives... 2 2. Definitions... 2 3. Type of Association Rules... 7 4. Frequent Itemset generation... 9 5. Apriori Algorithm: Mining Single-Dimension Boolean AR 13 5.1. Join Step:...

More information

PLANE GEOMETRY SKILL BUILDER ELEVEN

PLANE GEOMETRY SKILL BUILDER ELEVEN PLANE GEOMETRY SKILL BUILDER ELEVEN Lines, Segments, and Rays The following examples should help you distinguish between lines, segments, and rays. The three undefined terms in geometry are point, line,

More information

Topic 6: Parallel and Perpendicular

Topic 6: Parallel and Perpendicular Topic 6: Parallel and Perpendicular for use after The Shapes of Algebra Investigation 2 In the diagram below, lines O, m, n, and p are parallel lines. The other two lines are transversals. Angles ACI and

More information

CHAPTER TWO. . Therefore the oblong number n(n + 1) is double the triangular number T n. , and the summands are the triangular numbers T n 1 and T n.

CHAPTER TWO. . Therefore the oblong number n(n + 1) is double the triangular number T n. , and the summands are the triangular numbers T n 1 and T n. CHAPTER TWO 1. Since AB BC; since the two angles at B are equal; and since the angles at A and C are both right angles, it follows by the angle-side-angle theorem that EBC is congruent to SBA and therefore

More information

AAS Triangle Congruence

AAS Triangle Congruence Name Date Class 6-2 AAS Triangle Congruence Practice and Problem Solving: A/B 1. Students in Mrs. Marquez s class are watching a film on the uses of geometry in architecture. The film projector casts the

More information

6.4 rectangles 2016 ink.notebook. January 22, Page 22. Page Rectangles. Practice with. Rectangles. Standards. Page 24.

6.4 rectangles 2016 ink.notebook. January 22, Page 22. Page Rectangles. Practice with. Rectangles. Standards. Page 24. 6.4 rectangles 2016 ink.notebook Page 22 Page 23 6.4 Rectangles Practice with Rectangles Lesson Objectives Standards Lesson Notes Page 24 6.4 Rectangles Press the tabs to view details. 1 Lesson Objectives

More information

Association rules. Marco Saerens (UCL), with Christine Decaestecker (ULB)

Association rules. Marco Saerens (UCL), with Christine Decaestecker (ULB) Association rules Marco Saerens (UCL), with Christine Decaestecker (ULB) 1 Slides references Many slides and figures have been adapted from the slides associated to the following books: Alpaydin (2004),

More information

Chapter 4: Mining Frequent Patterns, Associations and Correlations

Chapter 4: Mining Frequent Patterns, Associations and Correlations Chapter 4: Mining Frequent Patterns, Associations and Correlations 4.1 Basic Concepts 4.2 Frequent Itemset Mining Methods 4.3 Which Patterns Are Interesting? Pattern Evaluation Methods 4.4 Summary Frequent

More information

b The orders of the vertices are 29, 21, 17 and 3 The graph is neither Eulerian not semi-eulerian since it has more than 2 odd vertices.

b The orders of the vertices are 29, 21, 17 and 3 The graph is neither Eulerian not semi-eulerian since it has more than 2 odd vertices. Route inspection Mied eercise 1 a The graph is Eulerian as all vertices are even. b The graph is neither as there are more than 2 odd nodes. 2 Any not connected graph with 6 even nodes, e.g. If the graph

More information

1Z

1Z 1Z0-257 Passing Score: 800 Time Limit: 4 min Exam A QUESTION 1 Which two statements are true about HFM Security? (Choose two.) A. Lightweight Directory Access Protocol (LDAP) groups can be added to HFM

More information

Downloaded from

Downloaded from Exercise 12.1 Question 1: A traffic signal board, indicating SCHOOL AHEAD, is an equilateral triangle with side a. Find the area of the signal board, using Heron s formula. If its perimeter is 180 cm,

More information

Name: Extra Midterm Review January 2018

Name: Extra Midterm Review January 2018 Name: Extra Midterm Review January 2018 1. Which drawing best illustrates the construction of an equilateral triangle? A) B) C) D) 2. Construct an equilateral triangle in which A is one vertex. A 3. Construct

More information

Chapter 2: Properties of Angles and Triangles

Chapter 2: Properties of Angles and Triangles Chapter 2: Properties of Angles and Triangles Section 2.1 Chapter 2: Properties of Angles and Triangles Section 2.1: Angle Properties and Parallel Lines Terminology: Transversal : A line that intersects

More information

7) Are HD and HA the same line?

7) Are HD and HA the same line? Review for Exam 2 Math 123 SHORT ANSWER. You must show all work to receive full credit. Refer to the figure to classify the statement as true or false. 7) Are HD and HA the same line? Yes 8) What is the

More information

Group Authentication Using The Naccache-Stern Public-Key Cryptosystem

Group Authentication Using The Naccache-Stern Public-Key Cryptosystem Group Authentication Using The Naccache-Stern Public-Key Cryptosystem Scott Guthery sguthery@mobile-mind.com Abstract A group authentication protocol authenticates pre-defined groups of individuals such

More information

DKT 122/3 DIGITAL SYSTEM 1

DKT 122/3 DIGITAL SYSTEM 1 Company LOGO DKT 122/3 DIGITAL SYSTEM 1 BOOLEAN ALGEBRA (PART 2) Boolean Algebra Contents Boolean Operations & Expression Laws & Rules of Boolean algebra DeMorgan s Theorems Boolean analysis of logic circuits

More information

Mining Functional Dependency from Relational Databases Using Equivalent Classes and Minimal Cover

Mining Functional Dependency from Relational Databases Using Equivalent Classes and Minimal Cover Journal of Computer Science 4 (6): 421-426, 2008 ISSN 1549-3636 2008 Science Publications Mining Functional Dependency from Relational Databases Using Equivalent Classes and Minimal Cover 1 Jalal Atoum,

More information

Greedy Algorithms and Matroids. Andreas Klappenecker

Greedy Algorithms and Matroids. Andreas Klappenecker Greedy Algorithms and Matroids Andreas Klappenecker Greedy Algorithms A greedy algorithm solves an optimization problem by working in several phases. In each phase, a decision is made that is locally optimal

More information

Introduction. The Quine-McCluskey Method Handout 5 January 24, CSEE E6861y Prof. Steven Nowick

Introduction. The Quine-McCluskey Method Handout 5 January 24, CSEE E6861y Prof. Steven Nowick CSEE E6861y Prof. Steven Nowick The Quine-McCluskey Method Handout 5 January 24, 2013 Introduction The Quine-McCluskey method is an exact algorithm which finds a minimum-cost sum-of-products implementation

More information

*Chapter 1.1 Points Lines Planes. Use the figure to name each of the following:

*Chapter 1.1 Points Lines Planes. Use the figure to name each of the following: Name: Period Date Pre- AP Geometry Fall 2015 Semester Exam REVIEW *Chapter 1.1 Points Lines Planes Use the figure to name each of the following: 1) three non-collinear points 2) one line in three different

More information

Unit 2A: Angle Pairs and Transversal Notes

Unit 2A: Angle Pairs and Transversal Notes Unit 2A: Angle Pairs and Transversal Notes Day 1: Special angle pairs Day 2: Angle pairs formed by transversal through two nonparallel lines Day 3: Angle pairs formed by transversal through parallel lines

More information

IMPLEMENTATION DESIGN FLOW

IMPLEMENTATION DESIGN FLOW IMPLEMENTATION DESIGN FLOW Hà Minh Trần Hạnh Nguyễn Duy Thái Course: Reconfigurable Computing Outline Over view Integra tion Node manipulation LUT-based mapping Design flow Design entry Functional simulation

More information

Chapter 6.1 Medians. Geometry

Chapter 6.1 Medians. Geometry Chapter 6.1 Medians Identify medians of triangles Find the midpoint of a line using a compass. A median is a segment that joins a vertex of the triangle and the midpoint of the opposite side. Median AD

More information

6.2 Initial Problem. Section 6.2 Network Problems. 6.2 Initial Problem, cont d. Weighted Graphs. Weighted Graphs, cont d. Weighted Graphs, cont d

6.2 Initial Problem. Section 6.2 Network Problems. 6.2 Initial Problem, cont d. Weighted Graphs. Weighted Graphs, cont d. Weighted Graphs, cont d Section 6.2 Network Problems Goals Study weighted graphs Study spanning trees Study minimal spanning trees Use Kruskal s algorithm 6.2 Initial Problem Walkways need to be built between the buildings on

More information

Combinational Logic Circuits

Combinational Logic Circuits Chapter 2 Combinational Logic Circuits J.J. Shann (Slightly trimmed by C.P. Chung) Chapter Overview 2-1 Binary Logic and Gates 2-2 Boolean Algebra 2-3 Standard Forms 2-4 Two-Level Circuit Optimization

More information

6-1 Study Guide and Intervention Angles of Polygons

6-1 Study Guide and Intervention Angles of Polygons 6-1 Study Guide and Intervention Angles of Polygons Polygon Interior Angles Sum The segments that connect the nonconsecutive vertices of a polygon are called diagonals. Drawing all of the diagonals from

More information

not to be republished NCERT CONSTRUCTIONS CHAPTER 10 (A) Main Concepts and Results (B) Multiple Choice Questions

not to be republished NCERT CONSTRUCTIONS CHAPTER 10 (A) Main Concepts and Results (B) Multiple Choice Questions CONSTRUCTIONS CHAPTER 10 (A) Main Concepts and Results Division of a line segment internally in a given ratio. Construction of a triangle similar to a given triangle as per given scale factor which may

More information

This Lecture. We will first introduce some basic set theory before we do counting. Basic Definitions. Operations on Sets.

This Lecture. We will first introduce some basic set theory before we do counting. Basic Definitions. Operations on Sets. Sets A B C This Lecture We will first introduce some basic set theory before we do counting. Basic Definitions Operations on Sets Set Identities Defining Sets Definition: A set is an unordered collection

More information

PREPRINT 2006:36. Airbag Folding Based on Origami Mathematics CHRISTOFFER CROMVIK KENNETH ERIKSSON

PREPRINT 2006:36. Airbag Folding Based on Origami Mathematics CHRISTOFFER CROMVIK KENNETH ERIKSSON PREPRINT 2006:36 Airbag Folding Based on Origami Mathematics CHRISTOFFER CROMVIK KENNETH ERIKSSON Department of Mathematical Sciences Division of Mathematics CHALMERS UNIVERSITY OF TECHNOLOGY GÖTEBORG

More information

Greedy Algorithms and Matroids. Andreas Klappenecker

Greedy Algorithms and Matroids. Andreas Klappenecker Greedy Algorithms and Matroids Andreas Klappenecker Greedy Algorithms A greedy algorithm solves an optimization problem by working in several phases. In each phase, a decision is made that is locally optimal

More information

Specifying logic functions

Specifying logic functions CSE4: Components and Design Techniques for Digital Systems Specifying logic functions Instructor: Mohsen Imani Slides from: Prof.Tajana Simunic and Dr.Pietro Mercati We have seen various concepts: Last

More information

Class IX Chapter 12 Heron's Formula Maths

Class IX Chapter 12 Heron's Formula Maths Class IX Chapter 12 Heron's Formula Maths 1: Exercise 12.1 Question A traffic signal board, indicating SCHOOL AHEAD, is an equilateral triangle with side a. Find the area of the signal board, using Heron

More information

GRAPH THEORY - FUNDAMENTALS

GRAPH THEORY - FUNDAMENTALS GRAPH THEORY - FUNDAMENTALS http://www.tutorialspoint.com/graph_theory/graph_theory_fundamentals.htm Copyright tutorialspoint.com A graph is a diagram of points and lines connected to the points. It has

More information

rm(list=ls(all=true)) # number of factors. # number of replicates.

rm(list=ls(all=true)) # number of factors. # number of replicates. # We have developed simple formulas that allow us to compute effects and the corresponding SS values (and, therefore F) from the contrast of each effect: effect = (1/(n*2^(k-1))) * contrast SS = ( 1/(n*2^k)

More information

15. K is the midpoint of segment JL, JL = 4x - 2, and JK = 7. Find x, the length of KL, and JL. 8. two lines that do not intersect

15. K is the midpoint of segment JL, JL = 4x - 2, and JK = 7. Find x, the length of KL, and JL. 8. two lines that do not intersect Name: Period Date Pre-AP Geometry Fall Semester Exam REVIEW *Chapter 1.1 Points Lines Planes Use the figure to name each of the following: 1. three non-collinear points 2. one line in three different ways

More information

Frequent Pattern Mining

Frequent Pattern Mining Frequent Pattern Mining...3 Frequent Pattern Mining Frequent Patterns The Apriori Algorithm The FP-growth Algorithm Sequential Pattern Mining Summary 44 / 193 Netflix Prize Frequent Pattern Mining Frequent

More information

Transactions in Euclidean Geometry

Transactions in Euclidean Geometry Transactions in Euclidean Geometry Volume 207F Issue # 8 Table of Contents Title Author Regular Triangles Cameron Hertzler Regular Pentagon Cameron Hertzler Hemispheres and Right Angles Cameron Hertzler

More information

4 KARNAUGH MAP MINIMIZATION

4 KARNAUGH MAP MINIMIZATION 4 KARNAUGH MAP MINIMIZATION A Karnaugh map provides a systematic method for simplifying Boolean expressions and, if properly used, will produce the simplest SOP or POS expression possible, known as the

More information

STAT 5200 Handout #28: Fractional Factorial Design (Ch. 18)

STAT 5200 Handout #28: Fractional Factorial Design (Ch. 18) STAT 5200 Handout #28: Fractional Factorial Design (Ch. 18) For factorial designs where all factors have 2 levels, it is possible to systematically exclude certain factor level combinations and still make

More information

Integrated Exercise 2 (Chapter 5 - Chapter 7)

Integrated Exercise 2 (Chapter 5 - Chapter 7) Integrated Exercise 2 (Chapter 5 - Chapter 7) Level 1 1 Each of the following pairs of triangles are either congruent or similar Write down the pair of congruent or similar triangles with reasons (a) (b)

More information

Presentation 4: Programmable Combinational Devices

Presentation 4: Programmable Combinational Devices Presentation 4: Programmable Combinational Devices Asst. Prof Dr. Ahmet ÖZKURT DEUEEE Based on the Presentation by Prof. Kim, Young Ho Dept. of Information Computer Engineering E-mail : yhkim@hyowon.cs.pusan.ac.kr

More information

ECE 3060 VLSI and Advanced Digital Design

ECE 3060 VLSI and Advanced Digital Design ECE 3060 VLSI and Advanced Digital Design Lecture 15 Multiple-Level Logic Minimization Outline Multi-level circuit representations Minimization methods goals: area, delay, power algorithms: algebraic,

More information

Stat 5303 (Oehlert): Unreplicated 2-Series Factorials 1

Stat 5303 (Oehlert): Unreplicated 2-Series Factorials 1 Stat 5303 (Oehlert): Unreplicated 2-Series Factorials 1 Cmd> a

More information

3. Given the similarity transformation shown below; identify the composition:

3. Given the similarity transformation shown below; identify the composition: Midterm Multiple Choice Practice 1. Based on the construction below, which statement must be true? 1 1) m ABD m CBD 2 2) m ABD m CBD 3) m ABD m ABC 1 4) m CBD m ABD 2 2. Line segment AB is shown in the

More information

COMP7640 Assignment 2

COMP7640 Assignment 2 COMP7640 Assignment 2 Due Date: 23:59, 14 November 2014 (Fri) Description Question 1 (20 marks) Consider the following relational schema. An employee can work in more than one department; the pct time

More information

Production rule is an important element in the expert system. By interview with

Production rule is an important element in the expert system. By interview with 2 Literature review Production rule is an important element in the expert system By interview with the domain experts, we can induce the rules and store them in a truth maintenance system An assumption-based

More information

Induction of Association Rules: Apriori Implementation

Induction of Association Rules: Apriori Implementation 1 Induction of Association Rules: Apriori Implementation Christian Borgelt and Rudolf Kruse Department of Knowledge Processing and Language Engineering School of Computer Science Otto-von-Guericke-University

More information

A Two-Phase Algorithm for Fast Discovery of High Utility Itemsets

A Two-Phase Algorithm for Fast Discovery of High Utility Itemsets A Two-Phase Algorithm for Fast Discovery of High Utility temsets Ying Liu, Wei-keng Liao, and Alok Choudhary Electrical and Computer Engineering Department, Northwestern University, Evanston, L, USA 60208

More information

Distributed Databases. CS347 Lecture 16 June 6, 2001

Distributed Databases. CS347 Lecture 16 June 6, 2001 Distributed Databases CS347 Lecture 16 June 6, 2001 1 Reliability Topics for the day Three-phase commit (3PC) Majority 3PC Network partitions Committing with partitions Concurrency control with partitions

More information

Assignment (3-6) Boolean Algebra and Logic Simplification - General Questions

Assignment (3-6) Boolean Algebra and Logic Simplification - General Questions Assignment (3-6) Boolean Algebra and Logic Simplification - General Questions 1. Convert the following SOP expression to an equivalent POS expression. 2. Determine the values of A, B, C, and D that make

More information

SAP C_HANATEC_12 Exam

SAP C_HANATEC_12 Exam Volume: 188 Questions Question No: 1 What are the recommended ways to perform a database backup?. A. Use the./hdbsetup command B. Use SQL commands C. Use the BRBACKUP command D. Use SAP HANA Studio Answer:

More information

Maharashtra Board Class IX Mathematics (Geometry) Sample Paper 1 Solution

Maharashtra Board Class IX Mathematics (Geometry) Sample Paper 1 Solution Maharashtra Board Class IX Mathematics (Geometry) Sample Paper 1 Solution Time: hours Total Marks: 40 Note: (1) All questions are compulsory. () Use of a calculator is not allowed. 1. i. In the two triangles

More information

GEOMETRY Final Exam Review First Semester

GEOMETRY Final Exam Review First Semester GEOMETRY Final Exam Review First Semester For questions 1-5, use the diagram shown as well as the word bank to complete each statement. In each case, list all that apply. Note: all terms in the word bank

More information

Lesson 8: Angle Angle Similarity

Lesson 8: Angle Angle Similarity : Angle Angle Similarity Learning Targets I can use the AA criteria to solve for missing angles or sides in triangle problems. I can prove two triangles to be similar by using Angle - Angle criteria Writing

More information

Question: 1 Which of the programming languages listed below are implemented plat for min dependently? Choose the correct answer(s).

Question: 1 Which of the programming languages listed below are implemented plat for min dependently? Choose the correct answer(s). Volume: 200 Questions Question: 1 Which of the programming languages listed below are implemented plat for min dependently? A. Fortran B. ABAP C. Java D. C/C++ Answer: B,C Question: 2 Which of the following

More information

ADVANCED EXERCISE 09B: EQUATION OF STRAIGHT LINE

ADVANCED EXERCISE 09B: EQUATION OF STRAIGHT LINE ADVANCED EXERCISE 09B: EQUATION OF STRAIGHT LINE It is given that the straight line L passes through A(5, 5) and is perpendicular to the straight line L : x+ y 5= 0 (a) Find the equation of L (b) Find

More information