data mining concepts and techniques ppt chapter 1

Data Mining Concepts and Techniques phần 5 ppt

Data Mining Concepts and Techniques phần 5 ppt

... Equations (6.50) and (6. 51) , we get w1 = (3 − 9 .1) (30 − 55.4) + (8 − 9 .1) (57 − 55.4) + · · · + (16 − 9 .1) (83 − 55.4) = 3.5 (3 − 9 .1) 2 + (8 − 9 .1) 2 + · · · + (16 − 9 .1) 2 w0 = 55.4 − (3.5)(9 .1) = 23.6 ... is the Table 6.7 Salary data x years experience y salary (in $10 00s) 30 57 64 13 72 36 43 11 59 21 90 20 16 83 Figure 6.26 Plot of the data in Table 6.7 for Example 6 .11 Although the points not ... distance between two points or tuples, say, X1 = (x 11 , x12 , , x1n ) and X2 = (x 21 , x22 , , x2n ), is n dist(X1 , X2 ) = ∑ (x1i − x2i )2 (6.45) i =1 In other words, for each numeric attribute,

Ngày tải lên: 08/08/2014, 18:22

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Data Mining Concepts and Techniques phần 6 ppt

Data Mining Concepts and Techniques phần 6 ppt

... of data tuples The bootstrap method works well with small data sets 14 e is the base of natural logarithms, that is, e = 2. 718 366 Chapter Classification and Prediction M1 New data sample M2 Data ... estimated as var(M1 ) var(M2 ) + , (6.70) var(M1 − M2 ) = k1 k2 and k1 and k2 are the number of cross-validation samples (in our case, 10 -fold crossvalidation rounds) used for M1 and M2 , respectively ... as follows: t= err(M1 ) − err(M2 ) , var(M1 − M2 )/k (6.68) where var(M1 − M2 ) = k ∑ err(M1 )i − err(M2 )i − (err(M1 ) − err(M2 )) k i =1 (6.69) To determine whether M1 and M2 are significantly

Ngày tải lên: 08/08/2014, 18:22

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Data Mining Concepts and Techniques phần 2 ppsx

Data Mining Concepts and Techniques phần 2 ppsx

... Chapter Data Preprocessing Data cleaning Data integration 22, 32, 10 0, 59, 48 Data reduction attributes A1 A2 A3 T1 T2 T3 T4 T2000 A126 transactions Data transformation ... Descriptive Data Summarization 59 Branch (unit price $) 12 0 11 0 10 0 90 80 70 60 50 40 40 50 60 70 80 90 Branch (unit price $) 10 0 11 0 12 0 Figure 2.6 A quantile-quantile plot for unit price data from ... a data mining query language can be used to specify data mining tasks In particular, we examine how to define data warehouses and data marts in our SQL-based data mining query language, DMQL Data

Ngày tải lên: 08/08/2014, 18:22

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Data Mining Concepts and Techniques phần 3 docx

Data Mining Concepts and Techniques phần 3 docx

... −3% 1% −2% 0% ? ?1% −9% 2% ? ?1% 0% 6% −3% −3% 4% −7% 1% 3% ? ?1% −39% 9% −34% 1% 18 % −2% 11 % 1% ? ?18 % 8% 5% Jul Aug Sep Oct Nov Dec 0% 4% −3% 5% −3% 1% −2% −8% −3% 7% ? ?1% 1% Figure 4 .17 Change in sales ... year dollars sold 10 01 TV 15 10 Q4 2003 250.60 10 02 TV 23 10 Q4 2003 17 5.00 50 01 TV all 10 Q4 2003 45,786.08 3.4 Data Warehouse Implementation 13 7 and 10 02 The day value ... month: R1, R2, R3 select 4.2 Further Development of Data Cube and OLAP Technology 19 5 such that R1.price = max(price) and R2 in R1 and R2.shelf = min(R1.shelf) and R3 in R1 and R3.shelf = max(R1.shelf)

Ngày tải lên: 08/08/2014, 18:22

78 453 1
Data Mining Concepts and Techniques phần 4 potx

Data Mining Concepts and Techniques phần 4 potx

... computer 15 0 12 00 North America computer 200 18 00 Table 4 .15 A crosstab for the sales in 2004 item TV location computer both items sales count sales count sales count Asia 15 300 12 0 10 00 13 5 13 00 ... Europe 12 250 15 0 12 00 16 2 14 50 North America 28 450 200 18 00 228 2250 all regions 45 10 00 470 4000 525 5000 Generalized data can be presented graphically, using bar charts, pie charts, and curves ... partitioning the data (mining on each partition and then combining the results) and sampling the data (mining on a subset of the data) These variations can reduce the number of data scans required

Ngày tải lên: 08/08/2014, 18:22

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Data Mining Concepts and Techniques phần 7 ppsx

Data Mining Concepts and Techniques phần 7 ppsx

... potential pairwise alignments, (a) and (b), of amino acids 516 Chapter Mining Stream, Time-Series, and Sequence Data (−8) + (−8) + (? ?1) + (−8) + (5) + (15 ) + (−8) + (10 ) + (6) + (−8) + (6) = Thus ... compare and align biological sequences and discover biosequence patterns 514 Chapter Mining Stream, Time-Series, and Sequence Data Before we get into further details, let’s look at the type of data ... frequent-pattern mining in Chapter 5, mining that is performed without user- or expert-specified constraints may generate numerous patterns that are 510 Chapter Mining Stream, Time-Series, and Sequence Data

Ngày tải lên: 08/08/2014, 18:22

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Data Mining Concepts and Techniques phần 9 pot

Data Mining Concepts and Techniques phần 9 pot

... standardize data mining products and to 11 .2 Data Mining System Products and Research Prototypes 663 ensure the interoperability of data mining systems Recent efforts at defining and standardizing data mining ... visualizer, and (multidimensional data) scatter visualizer for the visualization of data and data mining results 664 Chapter 11 Applications and Trends in Data Mining Oracle Data Mining (ODM), ... data mining products 11 .3 Additional Themes on Data Mining 11 .3 665 Additional Themes on Data Mining Due to the broad scope of data mining and the large variety of data mining methodologies,

Ngày tải lên: 08/08/2014, 18:22

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Data Mining Concepts and Techniques phần 10 pot

Data Mining Concepts and Techniques phần 10 pot

... the benefits of data mining in terms of time and money savings and the discovery of new knowledge 11 .5 Trends in Data Mining The diversity of data, data mining tasks, and data mining approaches ... content mining, Weblog mining, and data mining services on the Internet will become one of the most important and flourishing subfields in data mining Distributed data mining: Traditional data mining ... patterns, improved handling of complex data types and stream data, real-time data mining, Web mining, and so on In addition, the integration of data mining into existing business and scientific technologies,

Ngày tải lên: 08/08/2014, 18:22

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Khai thác đồ thị dựa trên tài liệu data mining concepts and techniques, jiawei han

Khai thác đồ thị dựa trên tài liệu data mining concepts and techniques, jiawei han

... NHÂN–09DH 111 81 TRẦN BÌNH AN – VƯU VĨNH PHÚC- ĐỒ ÁN MƠN HỌC KHAI THÁC DỮ LIỆU VÀ ỨNG DỤNG ĐỀ TÀI : KHAI THÁC ĐỒ THỊ DỰA TRÊN TÀI LIỆU : Data Mining: Concepts and Techniques, Jiawei Han TP.HCM – 12 /2 012 ... V.v… 9 .1. 1 Các phương thức khai thác đồ thị phổ biến ⊆⊆ Đầu tiên ta giới thiệu khái niệm đồ thị con: Cho hai đồ thị G(V,E) G1(V1,E1) ta đồ thị G1 đồ thị G V1 V E1 e=(i,j) thuộc V G, e thuộc V1 i, ... – Bước1: Làm đồ thị cách xóa cạnh khơng thỏa mãn độ hỗ trợ (b) 15 Hình 19 : Ví dụ làm đồ thị gSpan – Step 2: Tìm tất cạnh đơn phổ biến, cạnh có độ hỗ trợ lớn {(a_5,c_3),(a_6,c _1) } => (0 ,1, a,c)

Ngày tải lên: 12/11/2015, 13:20

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Data mining  concepts and techniques   jiawei han, micheline kamber   2nd edition

Data mining concepts and techniques jiawei han, micheline kamber 2nd edition

... satisfies 25 2.8 EXERCISES T1 T2 T3 T4 T5 T6 T7 T8 T9 Tuples T10 22 T 11 25 T12 25 T13 25 T14 25 T15 30 T16 33 T17 33 T18 33 13 15 16 16 19 20 20 21 22 T19 T20 T 21 T22 T23 T24 T25 T26 T27 33 ... two-dimensional data set: x1 x2 x3 x4 x5 A1 1. 5 1. 6 1. 2 1. 5 A2 1. 7 1. 9 1. 8 1. 5 1. 0 (a) Consider the data as two-dimensional data points Given a new data point, x = (1. 4, 1. 6) as a query, rank the database ... 25 T16 33 T12 25 T17 33 T13 25 T18 33 T14 25 T19 33 T15 30 T20 35 T 21 T22 T23 T24 T25 Cluster sampling T6 20 T7 20 T8 21 T9 22 T10 22 T1 T2 T3 T4 T5 T6 T7 T8 T9 13 15 16 16 19 20 20 21 22 young

Ngày tải lên: 16/10/2021, 15:40

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Strategic management competitiveness globalization concepts and case 10e chapter 1

Strategic management competitiveness globalization concepts and case 10e chapter 1

... ACTIONSPART II: STRATEGIC • Chapters STRATEGY FORMULATION PART III: STRATEGIC ACTIONS10, 11 , 12 STRATEGY IMPLEMENTATION 13 & ©2 013 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, ... PART 1: STRATEGIC MANAGEMENT INPUTS CHAPTER 1: Strategic Management & Strategic Competitiveness Authored by: Marta Szabo White Ph.D Georgia State University FIGURE 1. 1 The Strategic ... successfully formulates and implements a value-creating strategy ● STRATEGY - an integrated and coordinated set of commitments and actions designed to exploit core competencies and gain a competitive

Ngày tải lên: 09/12/2016, 15:05

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Nanotechnology and the Environment - Chapter 1 ppt

Nanotechnology and the Environment - Chapter 1 ppt

... Nanotechnology and the Environment Chapter 10 Nanoparticle Use in Pollution Control 225 Kathleen Sellers Chapter 11 Balancing the Risks and Rewards 249 Kathleen Sellers 6 019 8.indb 6 6 /12 /08 1: 31: 29 PM ... compounds • • CONTENTS 1. 1 Potential Rewards 2 1. 2 Possible Risks and Public Concerns 3 1. 3 About This Book 8 References 9 6 019 8.indb 1 6 /12 /08 1: 31: 30 PM [...]... (e.g., Pesticide and Chemical News) ...  Introduction 14 00 Articles in Academic Journals 12 00 10 00 800 600 400 200 0 19 97 19 98 19 99 2000 20 01 2002 2003 2004 2005 2006 2007 19 97 19 98 19 99 2000 20 01 2002 2003 2004...

Ngày tải lên: 18/06/2014, 22:20

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DEFINITIONS CONVERSIONS and CALCULATIONS for OCCUPATIONAL SAFETY and HEALTH PROFESSIONALS - CHAPTER 1 ppt

DEFINITIONS CONVERSIONS and CALCULATIONS for OCCUPATIONAL SAFETY and HEALTH PROFESSIONALS - CHAPTER 1 ppt

... of Boyle's Law, Equation #1- 5, from Page 11 7: P1V1 = P2 V2 [Eqn #1- 5] (17 5)V1 = (1, 013 .25) (10 0) = 1. 013 25 × 10 V1 = 1. 013 24 × 10 = 579 17 5 ∴ V1 = 579 liters Problem #1. 6: The solution to this ... using Equation #1- 1, from Page 1- 16: t Metric + 273 .16 = TMetric [Eqn #1- 1] 18 + 273 .16 = TMetric = 2 91. 16 K Now we must join Equation #s 1- 9 & 1- 10, from Pages 1- 18, 1- 19, & 1- 20, into a “combined” ... using Equation #1- 1, from Page 1- 16: t Metric + 273 .16 = TMetric [Eqn #1- 1] 31 + 273 .16 = TMetric = 304 .16 K Now we must again join Equation #s 1- 9 & 1- 10, from Pages 1- 18, 1- 19, & 1- 20, into a

Ngày tải lên: 10/08/2014, 20:20

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The Water Encyclopedia: Hydrologic Data and Internet Resources - Chapter 1 pps

The Water Encyclopedia: Hydrologic Data and Internet Resources - Chapter 1 pps

... significantly expanded beyond the previous edition. The first two chapters of this edition are new and discuss data management and international data collection. Data management concepts are presented ... review the use of databases, geographic information systems (GIS), data reporting and metadata. Data repositories and availability vary around the world and range in ease of access and usability. ... water-related data. This edition contains more than 11 00 tables and 500 figures providing data related to weather, surface water, groundwater, water use, water quality, waste water, pollution, and water

Ngày tải lên: 11/08/2014, 21:21

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Microbiological Aspects of BIOFILMS and DRINKING WATER - Chapter 1 ppt

Microbiological Aspects of BIOFILMS and DRINKING WATER - Chapter 1 ppt

... 0590/frame/ch 01 Page Tuesday, April 11 , 2000 10 :05 AM Water Supply, Treatment, and Distribution CONTENTS 1. 1 1. 2 1. 3 1. 4 1. 5 1. 6 Water Supply A Short History of Water Supply and Treatment ... gas and sodium chloride and is shown in the following equation © 2000 by CRC Press LLC 0590/frame/ch 01 Page 11 Tuesday, April 11 , 2000 10 :05 AM Water Supply, Treatment, and Distribution 11 10 0 ... LLC 0590/frame/ch 01 Page 13 Tuesday, April 11 , 2000 10 :05 AM Water Supply, Treatment, and Distribution 13 1. 6 REFERENCES Anon., 19 96, Water and sanitation, WHO Fact Sheet No 11 2, World Health

Ngày tải lên: 12/08/2014, 05:21

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Data Structure and Algorithms CO2003 Chapter 1  Introduction

Data Structure and Algorithms CO2003 Chapter 1 Introduction

... e = ∗p ; 27 Pointers Example i n t main ( ) { i n t v1 = , v2 = ; i n t ∗ p1 , ∗ p2 ; p1 = &v1 ; p2 = &v2 ; ∗p1 = ; ∗p2 = ∗p1 ; p1 = p2 ; ∗p1 = ; c o u t ... operations that are meaningful for the data type Declaration of data Declaration of operations Encapsulation of data and operations Abstract data type Figure 1: Abstract data type model (source: Slideshare) ... in a specific order 13 21 34 Abstract data type The concept of abstraction: • Users know what a data type can • How it is done is hidden Definition An abstract data type is a data declaration packaged

Ngày tải lên: 29/03/2017, 18:21

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Data Mining Concepts and Techniques phần 1 potx

Data Mining Concepts and Techniques phần 1 potx

... 665 11 .3.2 Statistical Data Mining 666 11 .3.3 Visual and Audio Data Mining 667 11 .3.4 Data Mining and Collaborative Filtering 670 11 .4 Social Impacts of Data Mining 675 11 .4 .1 Ubiquitous and ... Prototypes 660 11 .2 .1 How to Choose a Data Mining System 660 11 .2.2 Examples of Commercial Data Mining Systems 663 11 .3 Additional Themes on Data Mining 665 11 .3 .1 Theoretical Foundations of Data Mining ... Data Mining 675 11 .4 .1 Ubiquitous and Invisible Data Mining 675 11 .4.2 Data Mining, Privacy, and Data Security 678 11 .5 Trends in Data Mining 6 81 11. 6 Summary 684 Exercises 685 Bibliographic Notes...

Ngày tải lên: 08/08/2014, 18:22

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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

... to Data Mining 1 Data Mining: Introduction Lecture Notes for Chapter 1 Introduction to Data Mining by Tan, Steinbach, Kumar © Tan,Steinbach, Kumar Introduction to Data Mining 8 Data Mining Tasks Prediction ... Introduction to Data Mining 29 Challenges of Data Mining Scalability Dimensionality Complex and Heterogeneous Data Data Quality Data Ownership and Distribution Privacy Preservation Streaming Data © Tan,Steinbach, ... of data Traditional techniques infeasible for raw data Data mining may help scientists – in classifying and segmenting data – in Hypothesis Formation © Tan,Steinbach, Kumar Introduction to Data...

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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

... data • curse of dimensionality – Can produce counter-intuitive results 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 vs d = 1. 414 2 d = 1. 414 2 ... rule) • R1: {A} => class (rule after adding conjunct) • Gain(R0, R1) = t [ log (p1/(p1+n1)) – log (p0/(p0 + n0)) ] • where t: number of positive instances covered by both R0 and R1 p0: number ... may vary from 1. 5m to 1. 8m • weight of a person may vary from 90lb to 300lb • income of a person may vary from $10 K to $1M © Tan,Steinbach, Kumar Introduction to Data Mining 40 1 nearest-neighbor Voronoi...

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