LECTURE NOTES & SOLUTIONS for Data Mining and Machine Learning: Fundamental Concepts and Algorithms 2nd Edition by Mohammed J. Zaki, Wagner Meira Jr

, Data Mining and Analysis:
Fundamental Concepts and Algorithms
dataminingbook.info


Mohammed J. Zaki1 Wagner Meira Jr.2

1
Department of Computer Science
Rensselaer Polytechnic Institute, Troy, NY, USA
2
Department of Computer Science
Universidade Federal de Minas Gerais, Belo Horizonte, Brazil


Chapter 1: Data Mining and Analysis




Zaki & Meira Jr. (RPI and UFMG) Data Mining and Analysis Chapter 1: Data Mining and Analysis

,Data Matrix
Data can often be represented or abstracted as an n × d data matrix, with n
rows and d columns, given as
 
X1 X2 · · · Xd
x1
 x11 x12 · · · x1d  
D =
x2 x21 x22 · · · x2d  
 .. .. .. .. .. 
. . . . . 
xn xn1 xn2 · · · xnd

Rows: Also called instances, examples, records, transactions, objects,
points, feature-vectors, etc. Given as a d-tuple

xi = (xi1 , xi2 , . . . , xid )

Columns: Also called attributes, properties, features, dimensions,
variables, f ields, etc. Given as an n-tuple

Xj = (x1j , x2j , . . . , xnj )

Zaki & Meira Jr. (RPI and UFMG) Data Mining and Analysis Chapter 1: Data Mining and Analysis

, Iris Dataset Extract

 
Sepal Sepal Petal Petal
Class

 length width length width 


 X1 X2 X3 X4 X5 

 x1 5.9 3.0 4.2 1.5 Iris-versicolor
 
 x2 6.9 3.1 4.9 1.5 Iris-versicolor
 
 x3 6.6 2.9 4.6 1.3 Iris-versicolor
 
 x4 4.6 3.2 1.4 0.2 Iris-setosa 
 
 x5 6.0 2.2 4.0 1.0 Iris-versicolor
 
 x6 4.7 3.2 1.3 0.2 Iris-setosa 
 
 x7 6.5 3.0 5.8 2.2 Iris-virginica 
 
 x8 5.8 2.7 5.1 1.9 Iris-virginica 
 
 .. .. .. .. .. .. 
 . . . . . . 
 
x149 7.7 3.8 6.7 2.2 Iris-virginica 
x150 5.1 3.4 1.5 0.2 Iris-setosa



Zaki & Meira Jr. (RPI and UFMG) Data Mining and Analysis Chapter 1: Data Mining and Analysis