DATA MINING from data to information Ronald Westra Dep. Mathematics Knowledge Engineering

DATA MINING from data to information

Ronald WestraDep. MathematicsKnowledge EngineeringMaastricht University

PART 1

Introduction

All information on math-part of course on:

http://www.math.unimaas.nl/personal/ronaldw/DAM/DataMiningPage.htm



Data mining - a definition

"Data mining is the process of exploration and analysis, by automatic or semi-automatic means, of large quantities of data

in order to discover meaningful patterns and results."

(Berry & Linoff, 1997, 2000)

DATA MINING

Course Description:

In this course the student will be made familiar with the main topics in Data Mining, and its important role in current Computer Science. In this course we’ll mainly focus on algorithms, methods, and techniques for the representation and analysis of data and information.

DATA MINING

Course Objectives:

To get a broad understanding of data mining and knowledge discovery in databases.

To understand major research issues and techniques in this new area and conduct research.

To be able to apply data mining tools to practical problems.

LECTURE 1: Introduction

1. Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996), Data Mining to Knowledge Discovery in Databases: http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf

2. Hand, D., Manilla, H., Smyth, P. (2001), Principles of Data Mining, MIT press, Boston, USA MORE INFORMATION ON: ELEUMand:http://www.math.unimaas.nl/personal/ronaldw/DAM/DataMiningPage.htm

http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf

http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf

Hand, D., Manilla, H., Smyth, P. (2001),

Principles of Data Mining,

MIT press, Boston, USA

+ MORE INFORMATION ON: ELEUM or DAM-website


What is Data Mining?• data information knowledge

• patterns structures models

The use of Data Mining• increasingly larger databases TB (TeraBytes)

• N datapoints and K components (fields) per datapoint

• not accessible for fast inspection

• incomplete, noise, wrong design

• different numerical formats, alfanumerical, semantic fields

• necessity to automate the analysis


Applications

• astronomical databases

• marketing/investment

• telecommunication

• industrial

• biomedical/genetica


Historical Context

• in mathematical statistics negative connotation:

• danger for overfitting and erroneous generalisation


Data Mining Subdisciplines

• Databases

• Statistics

• Knowledge Based Systems

• High-performance computing

• Data visualization

• Pattern recognition

• Machine learning


Data Mining -methodes

• Clustering

• classification (off- & on-line)

• (auto)-regression

• visualisation techniques: optimal projections and PCA (principal component analysis)

• discrimnant analysis

• decomposition

• parameteriical modelling

• non-parameteric modeling


Data Mining essentials

• model representation

• model evaluation

• search/optimisation

Data Mining algorithms

• Decision trees/Rules

• Nonlinear Regression and Klassificatie

• Example-based methods

• AI-tools: NN, GA, ...


Data Mining and Mathematical Statistics

• when Statistics and when DM?

• is DM a sort of Mathematical Statistics?

Data Mining and AI

• AI is instrumental in finding knowledge in large chunks of data

Mathematical Principles in Data Mining

Part I: Exploring Data Space

* Understanding and Visualizing Data Space

Provide tools to understand the basic structure in databases. This is done by probing and analysing metric structure in data-space, comprehensively visualizing data, and analysing global data structure by e.g. Principal Components Analysis and Multidimensional Scaling.

* Data Analysis and Uncertainty

Show the fundamental role of uncertainty in Data Mining. Understand the difference between uncertainty originating from statistical variation in the sensing process, and from imprecision in the semantical modelling. Provide frameworks and tools for modelling uncertainty: especially the frequentist and subjective/conditional frameworks.


PART II: Finding Structure in Data Space

* Data Mining Algorithms & Scoring Functions

Provide a measure for fitting models and patterns to data. This enables the selection between competing models. Data Mining Algorithms are discussed in the parallel course.

* Searching for Models and Patterns in Data Space

Describe the computational methods used for model and pattern-fitting in data mining algorithms. Most emphasis is on search and optimisation methods. This is required to find the best fit between the model or pattern with the data. Special attention is devoted to parameter estimation under missing data using the maximum likelihood EM-algorithm.


PART III: Mathematiscal Modelling of Data Space

* Descriptive Models for Data Space

Present descriptive models in the context of Data Mining. Describe specific techniques and algorithms for fitting descriptive models to data. Main emphasis here is on probabilistic models.

* Clustering in Data Space

Discuss the role of data clustering within Data Mining. Showing the relation of clustering in relation to classification and search. Present a variety of paradigms for clustering data.

EXAMPLES

* Astronomical Databases

* Phylogenetic trees from DNA-analysis

Example 1: Phylogenetic Trees

The last decade has witnessed a major and historical leap in biology and all related disciplines. The date of this event can be set almost exactly to November 1999 as the Humane Genome Project (HGP) was declared completed. The HGP resulted in (almost) the entire humane genome, consisting of about 3.3.109 base pairs (bp) code, constituting all approximately 35K humane genes. Since then the genomes of many more animal and plant species have come available. For our sake, we can consider the humane genome as a huge database, existing of a single string with 3.3.109 characters from the set {C,G,A,T}.


This data constitutes the human ‘source code’. From this data – in principle – all ‘hardware’ characteristics, such as physiological and psychological features, can be deduced. In this block we will concentrate on another aspect that is hidden in this information: phylogenetic relations between species. The famous evolutionary biologist Dobzhansky once remarked that: ‘Everything makes sense in the light of evolution, nothing makes sense without the light of evolution’. This most certainly applies to the genome. Hidden in the data is the evolutionary history of the species. By comparing several species with various amount of relatedness, we can from systematic comparison reconstruct this evolutionary history. For instance, consider a species that lived at a certain time in earth history. It will be marked by a set of genes, each with a specific code (or rather, a statistical variation around the average).


If this species is by some reason distributed over a variety of non-connected areas (e.g. islands, oases, mountainous regions), animals of the species will not be able to mate at a random. In the course of time, due to the accumulation of random mutations, the genomes of the separated groups will increasingly differ. This will result in the origin of sub-species, and eventually new species. Comparing the genomes of the new species will shed light on the evolutionary history, in that: we can draw a phylogenetic tree of the sub-species leading to the ‘founder’-species; given the rate of mutation we can estimate how long ago the founder-species lived; reconstruct the most probable genome of the founder-species.

Example 2: data mining in astronomy



DATA AS SETS OF MEASUREMENTS AND

OBSERVATIONS

Data Mining Lecture II[Chapter 2 from Principles of Data Mining by

Hand,, Manilla, Smyth ]

LECTURE 2: DATA AS SETS OF MEASUREMENTS AND OBSERVATIONS

Readings:

• Chapter 2 from Principles of Data Mining by Hand, Mannila, Smyth.

2.1 Types of Data

2.2 Sampling 1. (re)sampling

2. oversampling/undersampling, sampling artefacts

3. Bootstrap and Jack-Knife methodes

2.3 Measures for Similarity and Difference1. Phenomenological

2. Dissimilarity coefficient

3. Metric in Data Space based on distance measure

Types of data

Sampling :

– the process of collecting new (empirical) data

Resampling :

– selecting data from a larger already existing collection

Sampling

–Oversampling

–Undersampling

–Sampling artefacts (aliasing, Nyquist frequency)

Sampling artefacts (aliasing, Nyquist frequency)

Moire fringes

http://upload.wikimedia.org/wikipedia/commons/3/31/Moire_pattern_of_bricks.jpg

Resampling

Resampling is any of a variety of methods for doing one of the following:

– Estimating the precision of sample statistics (medians, variances, percentiles) by using subsets of available data (= jackknife) or drawing randomly with replacement from a set of data points (= bootstrapping)

– Exchanging labels on data points when performing significance tests (permutation test, also called exact test, randomization test, or re-randomization test)

– Validating models by using random subsets (bootstrap, cross validation)

Bootstrap & Jack-Knife methodes

using inferential statistics to account for randomness and uncertainty in the observations. These inferences may take the form of answers to essentially yes/no questions (hypothesis testing), estimates of numerical characteristics (estimation), prediction of future observations, descriptions of association (correlation), or modeling of relationships (regression).

Bootstrap method

bootstrapping is a method for estimating the sampling distribution of an estimator by resampling with replacement from the original sample.

"Bootstrap" means that resampling one available sample gives rise to many others, reminiscent of pulling yourself up by your bootstraps.

cross-validation: verify replicability of results

Jackknife: detect outliers

Bootstrap: inferential statistics

2.3 Measures for Similarity and Dissimilarity

1. Phenomenological

2. Dissimilarity coefficient

3. Metric in Data Space based on distance measure

2.4 Distance Measure and Metric

1. Euclidean distance

2. Metric

3. Commensurability

4. Normalisatie

5. Weighted Distances

6. Sample covariance

7. Sample covariance correlation coefficient

8. Mahalanobis distance

9. Normalised distance and Cluster Separation (zie aanvullende tekst)

10. Generalised Minkowski




2. Generalized p-norm

Generalized Norm / Metric

Minkowski Metric

Minkowski Metric

Generalized Minkowski Metric

In the data space is already a structure present.

The structure is represented by the correlation and given by the covariance matrix G

The Minkowski-norm of a vector x is:

xGxx T 2



2. 2. Metric

3. Commensurability

4. Normalisatie

5. Weighted Distances

6. Sample covariance

7. Sample covariance correlation coefficient


9. Normalised distance and Cluster Separation (zie aanvullende tekst)

10. Generalised Minkowski


Mahalanobis distance



The Mahalanobis distance is a distance measure introduced by P. C. Mahalanobis in 1936.

It is based on correlations between variables by which different patterns can be identified and analysed. It is a useful way of determining similarity of an unknown sample set to a known one.

It differs from Euclidean distance in that it takes into account the correlations of the data set.



The Mahalanobis distance from a group of values with mean

and covariance matrix Σ for a multivariate vector

is defined as:



Mahalanobis distance can also be defined as dissimilarity measure between two random vectors x and y of the same distribution with the covariance matrix Σ :



If the covariance matrix is the identity matrix then it is the same as Euclidean

distance. If covariance matrix is diagonal, then it is called normalized Euclidean distance:

where σi is the standard deviation of the xi over the sample set.

2.4 Distance measures and Metric






2.5 Distortions in Data Sets

1. outlyers

2. Variance

3. sampling effects

2.6 Pre-processong data with mathematical transformationes

2.7 Data Quality

• Data quality of individual measurements [GIGO]

• Data quality of Data collections

Part II. Exploratory Data Analysis

VISUALISING AND EXPLORING DATA-SPACE

Data Mining Lecture III[Chapter 2 from Principles of Data Mining by

Hand,, Manilla, Smyth ]

LECTURE 3: Visualising and Exploring Data-Space

Readings:

• Chapter 3 from Principles of Data Mining by Hand, Mannila, Smyth.

3.1 Obtain insight in the Structure in Data Space

1. distribution over the space

2. Are there separate and disconnected parts?

3. is there a model?

4. data-driven hypothesis testing

5. Starting point: use strong perceptual powers of humans


3.2 Tools to represent a variabele

1. mean, variance, standard deviation, skewness

2. plot

3. moving-average plot

4. histogram, kernel

histogram


3.3 Tools for repressenting two variables

1. scatter plot

2. moving-average plots

scatter plot

scatter plots


3.4 Tools for representing multiple variables

1. all or selection of scatter plots

2. idem moving-average plots

3. ‘trelis’ or other parameterised plots

4. icons: star icons, Chernoff’s faces

Chernoff’s faces

Chernoff’s faces

Chernoff’s faces

3.5 PCA: Principal Component Ananlysis

3.6 MDS: Multidimensional Scaling

DIMENSION REDUCTION


1. With sub-scatter plots we already noticed that the best projections were determined by the projection that resulted in the optimal spreading of the set of points. This is in the direction of the smallest varaince. This idea is now worked out.

3.5 PCA: Principal Component Analysis

2. Underlying idea : supose you have a high-dimensional normal-distributed data set. This will take the shape of a high-dimensional ellipsoid.

An ellipsoid is structured from its centre by orthogonal vectors with different radii. The largest radii have the strongest influence on the shape of the ellipsoid. The ellipsoid is described by the covariance-matrix of the set of data-points. The axes are defined by the orthogonal eigen-vectors (from the centre – the centroid – of the set), the radii are defined by the associated values.

So determine the eigen-values and order those in decreasing size: .

The first n ordered eigen-vectors thus ‘explain’ the following amount of the data: .

},...,,,{ 321 N

N

ii

n

ii

11/




MEAN


MEAN

Principal axis 1

Principal axis 2


3. Plot the ordered eigen-values versus the index-number and inspect where a ‘shoulder’ occurs: this determines the number of eigen-values you take into acoount. This is a so-called ‘scree-plot’.


4. Other derivation is by maximization of the variance of the projected data geprojecteerde data (see book).

This leads to an Eigen-value problem of the covariance-matrix, so the solution described above.


5. For n points of p components there are: O(np2 + p3) operations required. Use LU-decomposition etcetera.


6. Many benefits: considerable data-reduction, necessary for computational techniques like ‘Fisher-discriminant-analysis’ and ‘clustering’.

This works very well in practice.


7. NB: Factor Analysis is often confused with PCA but is different:

the explanation of p-dimensional data by a smaller number of m < p factors.

Principal component analysis (PCA) is a technique that is useful for thecompression and classification of data. The purpose is to reduce thedimensionality of a data set (sample) by finding a new set of variables,smaller than the original set of variables, that nonetheless retains mostof the sample's information.

By information we mean the variation present in the sample,given by the correlations between the original variables. The newvariables, called principal components (PCs), are uncorrelated, and areordered by the fraction of the total information each retains.

overview

• geometric picture of PCs

• algebraic definition and derivation of PCs

• usage of PCA

• astronomical application

Geometric picture of principal components (PCs)

A sample of n observations in the 2-D space

Goal: to account for the variation in a sample in as few variables as possible, to some accuracy

Geometric picture of principal components (PCs)

• the 1st PC is a minimum distance fit to a line in space

PCs are a series of linear least squares fits to a sample,each orthogonal to all the previous.

• the 2nd PC is a minimum distance fit to a line in the plane perpendicular to the 1st PC

Algebraic definition of PCs

Given a sample of n observations on a vector of p variables

λ

where the vector

is chosen such that

define the first principal component of the sampleby the linear transformation

is maximum

Algebraic definition of PCs

Likewise, define the kth PC of the sampleby the linear transformation

λwhere the vector

is chosen such that is maximum

subject to

and to

Algebraic derivation of coefficient vectors

To find first note that

where

is the covariance matrix for the variables

To find maximize subject to


Let λ be a Lagrange multiplier

by differentiating…

then maximize

is an eigenvector of

corresponding to eigenvalue

therefore

We have maximized

Algebraic derivation of

So is the largest eigenvalue of

The first PC retains the greatest amount of variation in the sample.

To find the next coefficient vector maximize


then let λ and φ be Lagrange multipliers, and maximize

subject to

and to

First note that

We find that is also an eigenvector of


whose eigenvalue is the second largest.

In general

• The kth largest eigenvalue of is the variance of the kth PC.

• The kth PC retains the kth greatest fraction of the variation in the sample.

Algebraic formulation of PCA

Given a sample of n observations

define a vector of p PCs

according to

on a vector of p variables

whose kth column is the kth eigenvector of

where is an orthogonal p x p matrix

Then is the covariance matrix of the PCs,

being diagonal with elements

usage of PCA: Probability distribution for sample PCs

If (i) the n observations of in the sample are independent &

(ii) is drawn from an underlying population that follows a p-variate normal (Gaussian) distribution with known covariance matrix

then

where is the Wishart distribution

else utilize a bootstrap approximation


If (i) follows a Wishart distribution &

(ii) the population eigenvalues are all distinct

then the following results hold as

(a tilde denotes a population quantity)

• all the are independent of all the

• and

•

•

are jointly normally distributed


and

(a tilde denotes a population quantity)

•

•

usage of PCA: Inference about population PCs

If follows a p-variate normal distribution

MLE’s of , , and

confidence intervals for and

hypothesis testing for and

then analytic expressions exist* for

else bootstrap and jackknife approximations exist

*see references, esp. Jolliffe

usage of PCA: Practical computation of PCs

In general it is useful to define standardized variables by

If the are each measured about their sample mean

then the covariance matrix of

will be equal to the correlation matrix of

and the PCs will be dimensionless


Given a sample of n observations on a vector of p variables

(each measured about its sample mean)

compute the covariance matrix

where is the n x p matrix

Then compute the n x p matrix

whose ith row is the PC score

for the ith observation.

whose ith row is the ith obsv.


Write to decompose each observation into PCs

usage of PCA: Data compression

Because the kth PC retains the kth greatest fraction of the variation

we can approximate each observation

by truncating the sum at the first m < p PCs

usage of PCA: Data compression

Reduce the dimensionality of the data

from p to m < p by approximating

where is the n x m portion of

and is the p x m portion of

Dressler, et al. 1987

astronomical application: PCs for elliptical galaxies

Rotating to PC in BT – Σ space improves Faber-Jackson relation

as a distance indicator

astronomical application: Eigenspectra (KL transform)

Connolly, et al. 1995

references

Connolly, and Szalay, et al., “Spectral Classification of Galaxies: An Orthogonal Approach”, AJ, 110, 1071-1082, 1995.Dressler, et al., “Spectroscopy and Photometry of Elliptical Galaxies. I. A New Distance Estimator”, ApJ, 313, 42-58, 1987.Efstathiou, G., and Fall, S.M., “Multivariate analysis of elliptical galaxies”, MNRAS, 206, 453-464, 1984.Johnston, D.E., et al., “SDSS J0903+5028: A New Gravitational Lens”, AJ, 126, 2281-2290, 2003.

Jolliffe, Ian T., 2002, Principal Component Analysis (Springer-Verlag New York, Secaucus, NJ).Lupton, R., 1993, Statistics In Theory and Practice (Princeton University Press, Princeton, NJ).Murtagh, F., and Heck, A., Multivariate Data Analysis (D. Reidel Publishing Company, Dordrecht, Holland).Yip, C.W., and Szalay, A.S., et al., “Distributions of Galaxy Spectral Types in the SDSS”, AJ, 128, 585-609, 2004.


1 pc 2 pc

3 pc 4 pc

3.6 Multidimensional Scaling [MDS]

1. Same purpose : represent high-dimensional data set

2. In the case of MS not by projections, but by reconstruction from the distance-table. The computed points are represented in an Euclidian sub-space – preferably a 2D-plane.

3. MDS performs better than PCA in case of strongly curved sets.

3.6 Multidimensional Scaling

The purpose of multidimensional scaling (MDS) is to provide a visual representation of the pattern of proximities (i.e., similarities or distances) among a set of objects

INPUT: distances dist[Ai,Aj] where A is some class of objects

OUTPUT: positions X[Ai] where X is a D-dimensional vector




INPUT: distances dist[Ai,Aj] where A is some class of objects


OUTPUT: positions X[Ai] where X is a D-dimensional vector


How many dimensions ??? SCREE PLOT

Multidimensional Scaling: Nederlandse dialekten

3.6 Kohonen’s Self Organizing Map (SOM) and Sammon mapping

1. Same purpose : DIMENSION REDUCTION : represent a high dimensional set in a smaller sub-space e.g. 2D-plane.

2. SOM gives better results than Sammon mapping, but strongly sensitive to initial values.

3. This is close to clustering!

3.6 Kohonen’s Self Organizing Map (SOM)

3.6 Kohonen’s Self Organizing Map (SOM)

Sammon mapping

1. Clustering versus Classification

• classification: give a pre-determined label to a sample

• clustering: provide the relevant labels for classification from structure in

a given dataset

• clustering: maximal intra-cluster similarity and maximal inter-cluster dissimilarity

• Objectives: - 1. segmentation of space

- 2. find natural subclasses

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DATA MINING from data to information Ronald Westra Dep. Mathematics Knowledge Engineering

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