Noise & Data Reduction. Paired Sample t Test Data Transformation - Overview From Covariance Matrix...

Noise & Data Reduction

Paired Sample t Test Data Transformation - Overview From Covariance Matrix to PCA and Dimension

Reduction Fourier Analysis - Spectrum Dimension Reduction Data Integration Automatic Concept Hierarchy Generation

Testing Hypothesis

Remember:Central Limit Theorem

The sampling distribution of the mean of samples of size N approaches a normal (Gaussian) distribution as N approaches infinity.

If the samples are drawn from a population with mean and standard deviation , then the mean of the sampling distribution is and its standard deviation is as N increases.

These statements hold irrespective of the shape of the original distribution.

σ μσx =σ Nμ

Z Test standard deviation (population)

t Test sample standard deviation

• when population standard deviation is unknown, samples are small

population mean , sample mean

Z =x − μ

σ / N

t =x − μ

N −1∗ x i − x( )

∑€

N∗ x i − x( )

p Values Commonly we reject the H0 when the

probability of obtaining a sample statistic given the null hypothesis is low, say < .05

The null hypothesis is rejected but might be true

We find the probabilities by looking them up in tables, or statistics packages provide them The probability of obtaining a particular sample given

the null hypothesis is called the p value By convention, one usually dose not reject the null

hypothesis unless p < 0.05 (statistically significant)

Example Five cars parked, mean price of the cars is 20.270 €

and the standard deviation of the sample is 5.811€ The mean costs of cars in town is 12.000 €

(population) H0 hypothesis: parked cars are as expensive as the

cars in town

For N-1 (degrees of freedom) t=3.18 has a value less than 0.025, reject H0!

t =20270 −12000

5811/ 5= 3.18

Paired Sample t Test

Given a set of paired observations (from two normal populations)

A B =A-B

x1 y1 x1-x2

x2 y2 x2-y2

x3 y3 x3-y3

x4 y4 x4-y4

x5 y5 x5-y5

Calculate the mean and the standard deviation s of the the differences

H0: =0 (no difference)

H0: =k (difference is a constant)€

tδ =x δ − μδ

ˆ σ δ

ˆ σ δ =sδ

Confidence Intervals (σ known) Standard error from the standard deviation

95 Percent confidence interval for normal distribution is about the mean

σ x =σ Population

x ±1.96 ⋅σ x

Confidence interval when (σ unknown) Standard error from the sample standard deviation

95 Percent confidence interval for t distribution (t0.025 from a table) is

Previous Example:

x ± t0.025 ⋅ ˆ σ x

Zur Anzeige wird der QuickTime™ Dekompressor „TIFF (LZW)“

benötigt.

ˆ σ x =s

Overview Data Transformation

Reduce Noise Reduce Data

Data Transformation

Smoothing: remove noise from data Aggregation: summarization, data cube construction Generalization: concept hierarchy climbing Normalization: scaled to fall within a small, specified range

min-max normalization z-score normalization normalization by decimal scaling

Attribute/feature construction New attributes constructed from the given ones

Data Transformation: Normalization

Min-max normalization: to [new_minA, new_maxA]

Ex. Let income range $12,000 to $98,000 normalized to [0.0,

1.0]. Then $73,000 is mapped to

Z-score normalization (μ: mean, σ: standard deviation):

Ex. Let μ = 54,000, σ = 16,000. Then

Normalization by decimal scaling

716.00)00.1(000,12000,98

000,12600,73=+−

−−

minnewminnewmaxnewminmax

minvv _)__(' +−

−−

225.1000,16

000,54600,73=

How to Handle Noisy Data? (How to Reduce Features?)

Binning first sort data and partition into (equal-frequency) bins then one can smooth by bin means, smooth by bin

median, smooth by bin boundaries, etc. Regression

smooth by fitting the data into regression functions Clustering

detect and remove outliers Combined computer and human inspection

detect suspicious values and check by human (e.g., deal with possible outliers)

Data Reduction Strategies

A data warehouse may store terabytes of data Complex data analysis/mining may take a very long time to run on

the complete data set Data reduction

Obtain a reduced representation of the data set that is much smaller in volume but yet produce the same (or almost the same) analytical results

Data reduction strategies Data cube aggregation Dimensionality reduction—remove unimportant attributes Data Compression Numerosity reduction—fit data into models Discretization and concept hierarchy generation

Simple Discretization Methods: Binning

Equal-width (distance) partitioning: Divides the range into N intervals of equal size: uniform grid if A and B are the lowest and highest values of the attribute,

the width of intervals will be: W = (B –A)/N. The most straightforward, but outliers may dominate

presentation Skewed data is not handled well.

Equal-depth (frequency) partitioning: Divides the range into N intervals, each containing

approximately same number of samples Good data scaling Managing categorical attributes can be tricky.

Binning Methods for Data Smoothing

* Sorted data for price (in dollars): 4, 8, 9, 15, 21, 21, 24, 25, 26, 28, 29, 34* Partition into (equi-depth) bins: - Bin 1: 4, 8, 9, 15 - Bin 2: 21, 21, 24, 25 - Bin 3: 26, 28, 29, 34* Smoothing by bin means: - Bin 1: 9, 9, 9, 9 - Bin 2: 23, 23, 23, 23 - Bin 3: 29, 29, 29, 29* Smoothing by bin boundaries (min and max are identified, bin value replaced

by the closesed boundary value): - Bin 1: 4, 4, 4, 15 - Bin 2: 21, 21, 25, 25 - Bin 3: 26, 26, 26, 34

Cluster Analysis

Regression

y = x + 1

Heuristic Feature Selection Methods

There are 2d -1 possible sub-features of d features Several heuristic feature selection methods:

Best single features under the feature independence assumption: choose by significance tests

Best step-wise feature selection: • The best single-feature is picked first• Then next best feature condition to the first, ...

Step-wise feature elimination:• Repeatedly eliminate the worst feature

Best combined feature selection and elimination Optimal branch and bound:

• Use feature elimination and backtracking

Sampling: with or without Replacement

SRSWOR

(simple random

sample without

replacement)

Raw Data

Principal Component AnalysisFrom Covariance Matrix to PCA and Dimension Reduction

Feature space Sample

rx (1),

r x (2),..,

r x (k ),..,

r x (n )

⎪ ⎪ ⎪

∈ ℜ d

rx −

r y = (x i − y i)

Scaling

A well-known scaling method consists of performing some scaling operations subtracting the mean and dividing the standard deviation

mi sample mean si sample standard deviation

y i =(x i − mi)

According to the scaled metric the scaled feature vector is expressed as

shrinking large variance values si > 1

stretching low variance values si < 1

Fails to preserve distances when general linear transformation is applied!

||r y ||s=

(x i − mi)2

Covariance Measuring the tendency two features xi and xj

varying in the same direction The covariance between features xi and xj is

estimated for n patterns

c ij =

x i(k ) − mi( ) x j

(k ) − m j( )k=1

∑n −1

c11 c12 .. c1d

c21 c22 .. c2d

.. .. .. ..

cd1 cd 2 .. cdd

⎢ ⎢ ⎢ ⎢

⎥ ⎥ ⎥ ⎥

Correlation

Covariances are symmetric cij=cji

Covariance is related to correlation

x i(k ) − mi( ) x j

(k ) − m j( )k=1

∑(n −1)sis j

∈ −1,1[ ]

Karhunen-Loève Transformation Covariance matrix C of (a d d matrix)

Symmetric and positive definite

There are d eigenvalues and eigenvectors

is the i ith eigenvalue of C and ui the ith column of U, the ith eigenvectors

UTCU = Λ = diag(λ1,λ 2,...,λ d )

Cr u i = λ

r u i€

I − C( )u = 0

Eigenvectors are always orthogonal U is an orthonormal matrix UUT=UTU=I U defines the K-L transformation The transformed features by the K-L

transformation are given by

K-L transformation rotates the feature space into alignment with uncorrelated features

ry = U

r x (linear Transformation)

Example

1=2.618 2=0.382

u(1)=[1 0.618] u(2)=[-1 1.618]

C =2 1

⎣ ⎢

⎦ ⎥

I − C = 0

2 − 3λ +1= 0

−0.618 −1

−1 1.618

⎣ ⎢

⎦ ⎥u1

⎣ ⎢

⎦ ⎥= 0

benötigt.

PCA (Principal Components Analysis)

New features y are uncorrelated with the covariance Matrix

Each eigenvector ui is associated with some variance associated by i

Uncorrelated features with higher variance (represented by i) contain more information

Idea: Retain only the significant eigenvectors ui

Dimension Reduction

How many eigenvectors (and corresponding eigenvector) to retain

Kaiser criterion Discards eigenvectors whose eigenvalues

are below 1

Problems Principal components are linear

transformation of the original features

It is difficult to attach any semantic meaning to principal components

Fourier Analysis It is always possible to analyze „complex“

periodic waveforms into a set of sinusoidal waveforms

Any periodic waveform can be approximated by adding together a number of sinusoidal waveforms

Fourier analysis tells us what particular set of sinusoids go together to make up a particular complex waveform

Spectrum In the Fourier analysis of a complex

waveform the amplitude of each sinusoidal component depends on the shape of particular complex wave

• Amplitude of a wave: maximum or minimum deviation from zero line

• T duration of a period

benötigt.

Noise reduction or Dimension Reduction

It is difficult to identify the frequency components by looking at the original signal

Converting to the frequency domain

If dimension reduction, store only a fraction of frequencies (with high amplitude)

If noise reduction (remove high frequencies, fast change, smoothing) (remove low frequencies, slow change, remove global trends) Inverse discrete Fourier transform

benötigt.

Zur Anzeige wird der QuickTime™ Dekompressor „TIFF (Unkomprimiert)“

benötigt.

Zur Anzeige wird der QuickTime™ Dekompressor „TIFF (Unkomprimiert)“

benötigt.

Dimensionality Reduction:Wavelet Transformation

Discrete wavelet transform (DWT): linear signal processing, multi-resolutional analysis

Compressed approximation: store only a small fraction of the strongest of the wavelet coefficients

Similar to discrete Fourier transform (DFT), but better lossy compression, localized in space

Method: Length, L, must be an integer power of 2 (padding with 0’s, when necessary) Each transform has 2 functions: smoothing, difference Applies to pairs of data, resulting in two set of data of length L/2 Applies two functions recursively, until reaches the desired length

Haar2 Daubechie4

Data Integration

Data integration: Combines data from multiple sources into a coherent store

Schema integration: e.g., A.cust-id B.cust-# Integrate metadata from different sources

Entity identification problem: Identify real world entities from multiple data sources, e.g., Bill Clinton =

William Clinton Detecting and resolving data value conflicts

For the same real world entity, attribute values from different sources are different

Possible reasons: different representations, different scales, e.g., metric vs. British units

Handling Redundancy in Data Integration

Redundant data occur often when integration of multiple databases Object identification: The same attribute or object may have different

names in different databases Derivable data: One attribute may be a “derived” attribute in another

table, e.g., annual revenue

Redundant attributes may be able to be detected by correlation analysis

Careful integration of the data from multiple sources may help reduce/avoid redundancies and inconsistencies and improve mining speed and quality

Automatic Concept Hierarchy Generation

Some hierarchies can be automatically generated based on the analysis of the number of distinct values per attribute in the data set The attribute with the most distinct values is placed at the lowest level

of the hierarchy Exceptions, e.g., weekday, month, quarter, year

country

province_or_ state

street

15 distinct values

365 distinct values

3567 distinct values

674,339 distinct values

Paired Sample t Test Data Transformation - Overview From Covariance Matrix to PCA and Dimension

Reduction Fourier Analysis - Spectrum Dimension Reduction Data Integration Automatic Concept Hierarchy Generation

Mining Association rules Apriori Algorithm (Chapter 6, Han and

Kamber)

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