bits wase dm session 2

8/10/2019 Bits Wase Dm Session 2

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Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1

Data Mining: Data

Lecture Notes for Session 2

Introduction to Data Mining

byTan, Steinbach, Kumar

By Dr. K. Satyanarayan Reddy


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What is Data?

Collection of data objects and

their attributes.

An attribute is a property or

characteristic of an object

Examples: eye color of a

person, temperature, etc.

Attribute is also known as

variable, field, characteristic, or

feature & dimension.

A collection of Attributes

describe an Object

Object is also known as

Record, Point, Case, Vector,

Pattern, Sample, Entity,

Observationor Instance.

Tid

Refund

Marital

Status

Taxable

Income

Cheat

1 Yes

Single 125K No

2 No Married 100K No

3 No Single 70K No

4

Yes

Married 120K No

5 No Divorced 95K Yes

6 No Married 60K No

7 Yes

Divorced 220KNo

8 No Single 85K Yes

9 No Married 75K No

10 No Single 90K Yes10

Attributes

Objects


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Data

Definition 1: An attribute is a property or

characteristic of an object that may vary; eitherfrom one object to another or from one time to

another.

Definition 2: A measurement scale is a rule(function) that associates a numerical or symbolic

value with an attribute of an object.

The process of Measurementis the application of a

measurement scale to associate a value with aparticular attribute of a specific object.


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Attribute Values

Attribute values are numbers or symbols assignedto an attribute.

Distinction between attributes and attribute values

Same attribute can be mapped to different attributevalues

Example: height can be measured in feet or meters

Different attributes can be mapped to the same set of

valuesExample: Attribute values for ID and age are integers.

But properties of attribute values can be different

ID has no limit but age has a maximum and minimumvalue.


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Measurement of Length

The way you measure an attribute is somewhat may not match theattributes properties.

1

2

3

5

5

7

8

15

10 4

A

B

C

D

E


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Types of Attributes

There are different types of attributes

Nominal

Examples: ID numbers, eye color, zip codes

Ordinal

Examples: rankings (e.g., taste of potato chips on a scale

from 1-10), grades, height in {tall, medium, short}

Interval

Examples: calendar dates, temperatures in Celsius or

Fahrenheit.

Ratio

Examples: temperature in Kelvin, length, time, counts


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Properties of Attribute Values

The type of an attribute depends on which of the

following properties it possesses:

Distinctness: =

Order: < >

Addition: + - Multiplication: * /

Nominal attribute: distinctness

Ordinal attribute: distinctness & order Interval attribute: distinctness, order & addition

Ratio attribute: all 4 properties


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Types of Attributes

Nominal and ordinal attributes are collectively

referred to as Categoricalor Qualitativeattributes.

As the name suggests, qualitative attributes, such as

employee ID, lack most of the properties of

numbers.

The remaining two types of attributes, Intervaland

Ratio, are collectively referred to as Quantitativeor

Numeric Attributes.

Quantitative attributes are represented by numbersand have most of the properties of numbers.

Note: Quantitative attributes can be integer-valued or

continuous.


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Attribute

Type

Description Examples Operations

Nominal The values of a nominal attribute are

just different names, i.e., nominal

attributes provide only enoughinformation to distinguish one object

from another. (=, )

zip codes, employee

ID numbers, eye color,

sex: {male, female}

mode, entropy,

contingency

correlation, 2test

Ordinal The values of an ordinal attribute

provide enough information to order

objects. ()

hardness of minerals,

{good, better, best},

grades, street numbers

median, percentiles,

rank correlation,

run tests, sign tests

Interval For interval attributes, the

differences between values are

meaningful, i.e., a unit of

measurement exists.

(+, - )

calendar dates,

temperature in Celsius

or Fahrenheit

mean, standard

deviation, Pearson's

correlation, tandF

tests

Ratio For ratio variables, both differences

and ratios are meaningful. (*, /)

temperature in Kelvin,

monetary quantities,

counts, age, mass,

length, electrical

current

geometric mean,

harmonic mean,

percent variation


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Attribute

Level

Transformation Comments

Nominal Any permutation of values If all employee ID numbers

were reassigned, would itmake any difference?

Ordinal An order preserving change of

values, i.e.,

new_value = f(old_value)wherefis a monotonic function.

An attribute encompassing

the notion of good, better

best can be representedequally well by the values

{1, 2, 3} or by { 0.5, 1,

10}.

Interval new_value =a * old_value + b

where a and b are constants

Thus, the Fahrenheit and

Celsius temperature scales

differ in terms of wheretheir zero value is and the

size of a unit (degree).

Ratio new_value = a * old_value Length can be measured in

meters or feet.


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Discrete and Continuous Attributes

Discrete Attribute

Has only a finite or countably infinite set of values

Examples: zip codes, counts, or the set of words in a collection ofdocuments

Often represented as integer variables.

Note: binary attributes are a special case of discrete attributes

Continuous Attribute Has real numbers as attribute values

Examples: temperature, height, or weight.

Practically, real values can only be measured and represented using a finitenumber of digits.

Continuous attributes are typically represented as floating-point variables. Asymmetric Attributes: Binary attributes where only non-zero values are important

are called asymmetric binary attributes.

Example: If the number of credits associated with each course is recorded, then the

resulting data set will consist of asymmetric discrete or continuous attributes.


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Important Characteristics of Structured Data

Dimensionality: The dimensionality of a data set is the number of attributes that the objectsin the data set possess.

Curse of Dimensionality: Data with a small number of dimensions tends to be qualitativelydifferent than moderate or high-dimensional data.

The difficulties associated with analyzing high-dimensional data are sometimes referred to as the

curse of dimensionality.

Sparsity

Only Presence Counts: For some data sets, such as those with asymmetric features, most

attributes of an object have values of 0; in many cases fewer than 1% of the entries are non-zero.

In practical terms, sparsity is an advantage because usually only the non-zero values need to be

stored and manipulated. This results in significant savings with respect to computation time andstorage.

Resolution

Patterns depend on the scale

It is frequently possible to obtain data at different levels of resoIution, and often the properties of

the data are different at different resolutions.

Example: The surface of the Earth seems very uneven at a resolution of a few meters, but isrelatively smooth at a resolution of tens of kilometers.


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Record Data

Data that consists of a collection of records(data

objects), each of which consists of a fixed set ofdata fields (attributes).

Tid Refund Marital

Status

Taxable

Income Cheat

1 Yes Single 125K No

2 No Married 100K No

3 No Single 70K No

4 Yes Married 120K No

5 No Divorced 95K Yes

6 No Married 60KNo

7 Yes Divorced 220K No

8 No Single 85K Yes

9 No Married 75K No

10 No Single 90K Yes10


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Transaction or Market Basket Data

Transaction Data is a special type of record data, where each record (transaction) involves

a set of items.

Consider a grocery store where a set of products purchased by a customer during one

shopping trip constitutes a Transaction, while the individual products that were

purchased are the Items.

This type of data is called Market Basket Data because the items in each record are the

products in a person's "market basket."

Transaction data is a collection of sets of items, but it can

be viewed as a set of records whose fields are asymmetric

attributes.

Often, the attributes are binary, indicating whether or not an

item was purchased, but the attributes can be discrete orcontinuous, such as the number of items purchased or the

amount spent on those items.


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The Sparse Data Matrix

A Sparse Data Matrix is a special case

of a Data Matrix in which the attributes

are of the same type and are

asymmetric; i.e., only non-zero valuesare important.

Transaction data is an example of a

sparse data matrix that has only 0 - 1entries.

A common example is Document Data.


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Document Data

Each document becomes a `term' vector,

each term is a component (attribute) of the vector,

the value of each component is the number of times

the corresponding term occurs in the document.

Document 1

season

timeout

lost

wi

ngame

score

ball

play

coach

team

Document 2

Document 3

3 0 5 0 2 6 0 2 0 2

0

0

7 0 2 1 0 0 3 0 0

1 0 0 1 2 2 0 3 0


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Transaction Data

A special type of record data, where

each record (transaction) involves a set of items.

For example, consider a grocery store. The set of

products purchased by a customer during one

shopping trip constitute a transaction, while the

individual products that were purchased are the items.

TI D I tems

1 Bread, Coke, Milk

2 Beer, Bread

3 Beer, Coke, Diaper, Milk

4 Beer, Bread, Diaper, Milk

5 Coke, Diaper, Milk


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Graph Data contd

Examples: Generic graph and HTML Links

5

2

12

5

Data Mining

Graph Partitioning

Parallel Solution of Sparse Linear System of Equations

N-Body Computation and Dense Linear System Solvers

Linked Web pages


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Chemical Data

Benzene Molecule: C6H6


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Ordered Data

Sequences of Transactions: For some types of data, the

attributes have relationships that involve order in time or space.

An element of

the sequence

Items/Events


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Sequence Data Sequence data consists of a data set that is a

sequence of individual entities, such as a sequence of words orletters. It is quite similar to sequential data, except that there are

no time stamps; instead, there are positions in an ordered

sequence.

GGTTCCGCCTTCAGCCCCGCGCC

CGCAGGGCCCGCCCCGCGCCGTC

GAGAAGGGCCCGCCTGGCGGGCG

GGGGGAGGCGGGGCCGCCCGAGC

CCAACCGAGTCCGACCAGGTGCC

CCCTCTGCTCGGCCTAGACCTGAGCTCATTAGGCGGCAGCGGACAG

GCCAAGTAGAACACGCGAAGCGC

TGGGCTGCCTGCTGCGACCAGGG

Example: Genomic sequence data:

the genetic information of plants and

animals can be represented in the

form of sequences of nucleotides

that are known as genes.

Many of the problems associated

with genetic sequence data involvepredicting similarities in the structure

and function of genes from

similarities in nucleotide sequences.

Ordered Data contd.


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Ordered Data

Spatial Data: Some objects have spatial attributes, such as positions or areas,

as well as other types of attributes.

e.g.: Weather data (precipitation, temperature, pressure) that is collected for a

variety of geographical locations.

An important aspect of spatial data is spatial autocorrelation; i.e., objects that

are physically close tend to be similar in other ways as well. Thus, two points

on the Earth that are close to each other usually have similar values for

temperature and rainfall.

Spatio-Temporal Data:

Average Monthly

Temperature ofland and ocean


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Data Quality

Data Mining focuses on the

Detection and correction of data quality problems and

Use of algorithms that can tolerate poor data quality.

The first step, detection and correction, is often called Data

Cleaning.

What kinds of data quality problems?

How can we detect problems with the data?

What can we do about these problems?

Examples of data quality problems:

Noise and outliers

missing values

duplicate data


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Issues with Data

Measurement and Data Collection Issues: Perfect data cantbe expected. There may be

problems due to human error, limitations of measuring devices, or flaws in the data

collection process.

Values or even entire data objects may be missing or there may be spurious or duplicate

objects; i.e., multiple data objects that all correspond to a single "real" object.

e.g.: There might be two different records for a person who has recently lived at two

different addresses.

Even if all the data is present and "looks fine," there may be inconsistencies-a person

has a height of 2 meters, but weighs only 2 kilograms.

Measurement and Data Collection Errors: The term Measurement error refers to any

problem resulting from the measurement process it may be the value recordeddiffers

from the true value.

For continuous attributes, the numerical difference of the measured and true value is

called the error.

The term data collection error refers to errors such as omitting data objects or attribute

values, or inappropriately including a data object.

e.g.: A study of animals of a certain species might include animals of a related species

that are similar in appearance to the species of interest. Both measurement errors and

data collection errors can be either systematic or random.


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Noise

Noise refers to modification of original values

Examples: distortion of a personsvoice when talking

on a poor phone and snowon television screen

Two Sine Waves Two Sine Waves + Noise

d


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Precision, Bias and Accuracy

Precision: The closeness of repeated measurements (of the

same quantity) to one another.Bias: A systematic quantity being measured.

Precisionis often measured by the Standard Deviationof a set

of values, while Bias is measured by taking the difference

between the Meanof the set of values and the known valueof the quantity being measured.

Accuracy: The closeness of measurements to the true value of

the quantity being measured.

Accuracy depends on precision and bias, but there is nospecific formula for accuracy in terms of these two

quantities.

OnlyAccuracyis counted in terms of the of Significant Digits.

O li


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Outliers

Outliers are data objects with characteristics that

are considerably different than most of the otherdata objects in the data set

Mi i V l


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Missing Values

Reasons for Missing Values

Information is not collected(e.g., people decline to give their age and weight)

Attributes may not be applicable to all cases(e.g., annual income is not applicable to children)

Handling Missing Values Eliminate Data Objects

Estimate Missing Values

Ignore the Missing Value During Analysis

Replace with all possible values (weighted by their probabilities)

Inconsistent Values: Data can contain inconsistent values.

Consider an address field, where both a zip code and city are

listed, but the specified zip code area is not contained in that city.

D li t D t


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Duplicate Data

Data set may include data objects that are

duplicates, or almost duplicates of one another Major issue when merging data from heterogeous

sources

Examples:

Same person with multiple email addresses

Data cleaning Process of dealing with duplicate data issues

I R l t d t A li ti


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Issues Related to Applications

There are a few general issues:

Timeliness: Some data starts to age as soon as it has beencollected.

If the data is out of date, then so are the models and

patterns that are based on it.

Relevance: The available data must contain the informationnecessary for the application.

Knowledge about the Data: The data sets are accompanied

by documentation that describes different aspects of the

data; the quality of this documentation can either aid orhinder the subsequent analysis.

D t P i


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Data Preprocessing

Aggregation

Sampling

Dimensionality Reduction

Feature subset selection

Feature creation

Discretization and Binarization

Attribute Transformation

A ti


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Aggregation

Combining two or more attributes (or objects) into

a single attribute (or object)

Purpose

Data reductionReduce the number of attributes or objects

Change of scale

Cities aggregated into regions, states, countries, etc

More stabledataAggregated data tends to have less variability


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Aggregation

Standard Deviation of Average

Monthly Precipitation

Standard Deviation of Average

Yearly Precipitation

Variation of Precipitation in Australia


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Sampling

Sampling is the main technique employed for data

selection. It is often used for both the preliminary investigation of the

data and the final data analysis.

Statisticians sample because obtainingthe entire set ofdata of interest is too expensive or time consuming.

Sampling is used in data mining becauseprocessingtheentire set of data of interest is too expensive or timeconsuming.

S li


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Sampling

The key principle for effective sampling is the

following: using a sample will work almost as well as using the

entire data sets, if the sample is representative

A sample is representative if it has approximately thesame property (of interest) as the original set of data

Types of Sampling


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Types of Sampling

Simple Random Sampling

There is an equal probability of selecting any particular item

Sampling without replacement

As each item is selected, it is removed from the population

Sampling with replacement Objects are not removed from the population as they are

selected for the sample.

In sampling with replacement, the same object can be picked upmore than once

Stratified sampling

Split the data into several partitions; then draw random samplesfrom each partition

S l Si


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Sample Size

8000 points 2000 Points 500 Points


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Curse of Dimensionality


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Curse of Dimensionality

When dimensionality

increases, data becomesincreasingly sparse in the

space that it occupies

Definitions of density anddistance between points,

which is critical for

clustering and outlier

detection, become lessmeaningful

Randomly generate 500 points

Compute difference between max and min

distance between any pair of points



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Purpose:

Avoid curse of dimensionality Reduce amount of time and memory required by data

mining algorithms

Allow data to be more easily visualized

May help to eliminate irrelevant features or reducenoise

Techniques

Principle Component Analysis Singular Value Decomposition

Others: supervised and non-linear techniques

Dimensionality Reduction: PCA


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Dimensionality Reduction: PCA

Goal is to find a projection that captures the

largest amount of variation in data

x2

x1

e


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Dimensionality Reduction: ISOMAP


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Dimensionality Reduction: ISOMAP

Construct a neighbourhood graph

For each pair of points in the graph, compute the shortest

path distances geodesic distances

By: Tenenbaum, de Silva,

Langford (2000)


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Linear Algebra Techniques for Dimensionality Reduction


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Linear Algebra Techniques for Dimensionality Reduction

Principal Components Analysis (PCA) is a linear

algebra technique for continuous attributes thatfinds new attributes (principal components) that

are:-

1. linear combinations of the original attributes,

2. orthogonal (perpendicular) to each other, and

3. capture the maximum amount of variation in the

data.

Singular Value Decomposition (SVD) is a linear

algebra technique that is related to PCA and is

also commonly used for dimensionality reduction.

Feature Subset Selection


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Feature Subset Selection

Another way to reduce dimensionality of data

Redundant features

duplicate much or all of the information contained inone or more other attributes

Example: purchase price of a product and the amountof sales tax paid

Irrelevant features

contain no information that is useful for the datamining task at hand

Example: students' ID is often irrelevant to the task ofpredicting students' GPA

Feature Subset Selection contd


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Feature Subset Selection cont d.

Techniques:

Brute-force approach:

Try all possible feature subsets as input to data mining algorithm

Embedded approaches:

Feature selection occurs naturally as part of the data mining algorithm

Filter approaches:

Features are selected before data mining algorithm is run

Wrapper approaches:Use the data mining algorithm as a black box to find best subset of attributes

An Architecture for Feature Subset Selection


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An Architecture for Feature Subset Selection

As the number of subsets can be enormous and it is impractical to examine

them all, some sort of stopping criterion is necessary.

Once a subset of features has been selected, the results of the target data

mining algorithm on the selected subset should be validated.

Another approach for Validation is to use a number of different feature

selection algorithms to obtain subsets of features and then compare the

results of running the data mining algorithm on each subset. Feature Weighting: It is an alternative to keeping or eliminating

features. More important features are assigned a higher weight, while

less important features are given a lower weight.

Feature Creation: It is frequently possible to create, from the original

attributes, a new set of attributes that captures the important informationin a data set much more effectively.

Feature Extraction: The creation of a new set of features from the

original raw data is known as feature extraction.

e.g.: Extracting facial features of human, from set of Photographs.

Feature Creation


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Feature Creation

Create new attributes that can capture the important

information in a data set much more efficiently than the originalattributes

Three general methodologies:

Feature Extraction

domain-specific

Mapping Data to New Space: A totally different view of the

data can reveal important and interesting features.

Feature Construction: Sometimes the features in the

original data sets have the necessary information, but it is

not in a form suitable for the data mining algorithm.

combining features


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Discretization Using Class Labels


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Discretization Using Class Labels

it is often necessary to transform a continuous attribute into a categorical

attribute (discretization), and both continuous and discrete attributes may

need to be transformed into one or more binary attributes (binarization).

Entropy based approach:

3 categories for both x and y 5 categories for both x and y

Discretization Without Using Class Labels


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Discretization Without Using Class Labels

Data Equal interval width

Equal frequency K-means



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A function that maps the entire set of values of a

given attribute to a new set of replacement valuessuch that each old value can be identified with

one of the new values

Simple functions: xk, log(x), ex, |x|

Standardization and Normalization

Similarity and Dissimilarity


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Similarity and Dissimilarity

Similarity

Numerical measure of how alike two data objects are.

Is higher when objects are more alike.

Often falls in the range [0,1]

Dissimilarity Numerical measure of how different are two data

objects

Lower when objects are more alike

Minimum dissimilarity is often 0 Upper limit varies

Proximity refers to a similarity or dissimilarity

Similarity and Dissimilarity contd .


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Similarity/Dissimilarity for Simple Attributes

pand qare the attribute values for two data objects.

Similarity and Dissimilarity cont d.

Euclidean Distance


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Euclidean Distance

Euclidean Distance

Where nis the number of dimensions (attributes) andpkand qkare, respectively, the kth attributes (components) or dataobjectspand q.

Standardization is necessary, if scales differ.

n

kkk qpdist

1

2)(

Euclidean Distance


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Euclidean Distance

0

1

2

3

0 1 2 3 4 5 6

p1

p2

p3 p4

point x y

p1 0 2

p2 2 0

p3 3 1

p4 5 1

Distance Matrix

p1 p2 p3 p4

p1 0 2.828 3.162 5.099

p2 2.828 0 1.414 3.162

p3 3.162 1.414 0 2

p4 5.099 3.162 2 0

Minkowski Distance


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Minkowski Distance

Minkowski Distance is a generalization of Euclidean

Distance

Where r is a parameter, n is the number of dimensions(attributes) and pk and qk are, respectively, the kth attributes(components) or data objectspand q.

rn

k

rkk qpdist

1

1

)||(


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Minkowski Distance


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Minkowski Distance

Distance Matrix

point x y

p1 0 2

p2 2 0p3 3 1

p4 5 1

L1 p1 p2 p3 p4

p1 0 4 4 6p2 4 0 2 4

p3 4 2 0 2

p4 6 4 2 0

L2 p1 p2 p3 p4

p1 0 2.828 3.162 5.099

p2 2.828 0 1.414 3.162

p3 3.162 1.414 0 2

p4 5.099 3.162 2 0

L p1 p2 p3 p4

p1 0 2 3 5

p2 2 0 1 3

p3 3 1 0 2

p4 5 3 2 0

Mahalanobis Distance


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Tqpqpqpsmahalanobi )()(),( 1

For red points, the Euclidean distance is 14.7, Mahalanobis distance is 6.

is the covariance matrix of

the input data X

n

ikikjijkj XXXXn 1

, ))((1

1


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Mahalanobis Distance

Covariance Matrix:

3.02.0

2.03.0

B

A

C

A: (0.5, 0.5)

B: (0, 1)

C: (1.5, 1.5)

Mahal(A,B) = 5

Mahal(A,C) = 4

Common Properties of a Distance


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Common Properties of a Distance

Distances, such as the Euclidean distance,

have some well known properties.

1. d(p, q) 0 for all p and q and d(p, q) = 0 only ifp= q. (Positive definiteness)

2. d(p, q) = d(q, p) for allpand q. (Symmetry)

3. d(p, r) d(p, q) + d(q, r) for all points p, q, and r.(Triangle Inequality)

where d(p, q) is the distance (dissimilarity) betweenpoints (data objects),pand q.

A distance that satisfies these properties is ametric

Common Properties of a Similarity


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Co o ope t es o a S a ty

Similarities, also have some well known

properties.

1. s(p, q) = 1 (or maximum similarity) only if p = q.

2. s(p, q) = s(q, p) for all p and q. (Symmetry)

where s(p, q) is the similarity between points (dataobjects),pand q.

Similarity Between Binary Vectors


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y y

Common situation is that objects,pand q, have only

binary attributes Compute similarities using the following quantities

M01= the number of attributes where p was 0 and q was 1

M10 = the number of attributes where p was 1 and q was 0



Simple Matching and Jaccard CoefficientsSMC = number of matches / number of attributes

= (M11+ M00) / (M01+ M10+ M11+ M00)

J = number of 11 matches / number of not-both-zero attributes values

= (M11) / (M01+ M10+ M11)

SMC versus Jaccard: Example


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p

p= 1 0 0 0 0 0 0 0 0 0

q= 0 0 0 0 0 0 1 0 0 1

M01= 2 (the number of attributes where p was 0 and q was 1)

M10= 1 (the number of attributes where p was 1 and q was 0)

M00= 7 (the number of attributes where p was 0 and q was 0)M11= 0 (the number of attributes where p was 1 and q was 1)

SMC = (M11+ M00)/(M01+ M10+ M11+ M00) = (0+7) / (2+1+0+7) = 0.7

J = (M11) / (M01+ M10+ M11) = 0 / (2 + 1 + 0) = 0

Cosine Similarity


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y

If d1and d2are two document vectors, then

cos( d1, d2) = (d1 d2) / ||d1|| ||d2|| ,where indicates vector dot product and || d || is the length of vector d.

Example:

d1= 3 2 0 5 0 0 0 2 0 0

d2= 1 0 0 0 0 0 0 1 0 2

d1 d2= 3*1 + 2*0 + 0*0 + 5*0 + 0*0 + 0*0 + 0*0 + 2*1 + 0*0 + 0*2 = 5

||d1|| = (3*3+2*2+0*0+5*5+0*0+0*0+0*0+2*2+0*0+0*0)0.5= (42) 0.5= 6.481

||d2|| = (1*1+0*0+0*0+0*0+0*0+0*0+0*0+1*1+0*0+2*2)0.5= (6) 0.5= 2.245

cos( d1, d2) = .3150


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Visually Evaluating Correlation


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Scatter plots

showing thesimilarity from

1 to 1.

General Approach for Combining Similarities


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Sometimes attributes are of many different

types, but an overall similarity is needed.

Using Weights to Combine Similarities


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May not want to treat all attributes the same.

Use weights wkwhich are between 0 and 1 and sumto 1.


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Euclidean DensityCell-based


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Simplest approach is to divide region into a

number of rectangular cells of equal volume anddefine density as # of points the cell contains

Euclidean DensityCenter-based


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Euclidean density is the number of points within a

specified radius of the point


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THANK YOU

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