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### Data Mining(and machine learning)

ROC curves

Rule Induction

Basics of Text Mining

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Two classes is a common and special case

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Two classes is a common and special case

Medical applications: cancer, or not?

Computer Vision applications: landmine, or not?

Security applications: terrorist, or not?

Biotech applications: gene, or not?

… …

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Two classes is a common and special case

Medical applications: cancer, or not?

Computer Vision applications: landmine, or not?

Security applications: terrorist, or not?

Biotech applications: gene, or not?

… …

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Two classes is a common and special case

True Positive: these are ideal. E.g. we correctly detect cancer

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Two classes is a common and special case

True Positive: these are ideal. E.g. we correctly detect cancer

False Positive: to be minimised – cause false alarm – can be

better to be safe than sorry, but can be very costly.

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Two classes is a common and special case

True Positive: these are ideal. E.g. we correctly detect cancer

False Positive: to be minimised – cause false alarm – can be

better to be safe than sorry, but can be very costly.

False Negative: also to be minimised – miss a landmine / cancer

very bad in many applications

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Two classes is a common and special case

True Positive: these are ideal. E.g. we correctly detect cancer

False Positive: to be minimised – cause false alarm – can be

better to be safe than sorry, but can be very costly.

False Negative: also to be minimised – miss a landmine / cancer

very bad in many applications

True Negative?:

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Sensitivity and Specificity: common measures of accuracy in this kind of 2-class tasks

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Sensitivity and Specificity: common measures of accuracy in this kind of 2-class tasks

Sensitivity = TP/(TP+FN) - how much of the real ‘Yes’ cases

are detected? How sensitive is the classifier to ‘Yes’ cases?

Specificity = TN/(FP+TN) - how much of the real ‘No’ cases

are deteced?

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

YESNO

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

YESNO

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Specificity: 25%

YESNODavid Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Specificity: 50%

YESNODavid Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Specificity: 83.3%

YESNODavid Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Specificity: 100%

YESNODavid Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Sensitivity and Specificity: common measures of accuracy in this kind of 2-class tasks

Sensitivity = TP/(TP+FN) - how much of the real TRUE cases

are detected? How sensitive is the classifier to TRUE cases?

A highly sensitive test for cancer: if “NO” then you be sure it’s “NO”

Specificity = TN/(TN+FP) - how sensitive is the classifier to

the negative cases?

A highly specific test for cancer: if “Y” then you be sure it’s “Y”.

With many trained classifiers, you can ‘move the line’ in this way.

E.g. with NB, we could use a threshold indicating how much higher

the log likelihood for Y should be than for N

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

ROC curves

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Rule Induction

- Rules are useful when you want to learn a clear / interpretable classifier, and are less worried about squeezing out as much accuracy as possible
- There are a number of different ways to ‘learn’ rules or rulesets.
- Before we go there, what is a rule / ruleset?

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Rules

IF Condition … Then Class Value is …

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

A Ruleset

IF Condition1 … Then Class = A

IF Condition2 … Then Class = A

IF Condition3 … Then Class = B

IF Condition4 … Then Class = C

…

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Two ways to interpret a ruleset:

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Two ways to interpret a ruleset:

As a Decision List

IF Condition1 … Then Class = A

ELSE IF Condition2 … Then Class = A

ELSE IF Condition3 … Then Class = B

ELSE IF Condition4 … Then Class = C

…

ELSE … predict Majority Class

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Two ways to interpret a ruleset:

As an unordered set

IF Condition1 … Then Class = A

IF Condition2 … Then Class = A

IF Condition3 … Then Class = B

IF Condition4 … Then Class = C

Check each rule and gather votes for each class

If no winner, predict majority class

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Three broad ways to learn rulesets

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Three broad ways to learn rulesets

1. Just build a decision tree with ID3 (or something else) and you can translate the tree into rules!

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Three broad ways to learn rulesets

2. Use any good search/optimisation algorithm.

Evolutionary (genetic) algorithms are the most

common. You will do this coursework 3.

This means simply guessing a ruleset at random,

and then trying mutations and variants, gradually

improving them over time.

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Three broad ways to learn rulesets

3. A number of ‘old’ AI algorithms exist that still work well, and/or can be engineered to work with an evolutionary algorithm. The basic idea is: iterated coverage

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Text as Data: the basics

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

So, the most frequent words in a document

carry the most useful information ... ?

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Which is which

Some motivations for text mining

- Start your own company

http://www.text-analytics.com/comp.html

- Recommendations
- “if you like that, you might also like these …”
- On amazon, or any general product sales site, this can be based on distances between (e.g.) 200 word summaries or ToC of a book, or text that describes a product in a catalogue
- Document classification
- Coping with information overload
- Sentiment analysis ... Hotel reviews /product reviews

But a document is a “bag of words” – we need to convert to numbers

A one-slide text-mining tutorial

an article about poltics

NOW you can do

Clustering,

Retrieving similar

Documents,

Supervised

Classification

Etc...

(0.1, 0.2, 0, 0.02 ...)

(0.4, 0, 0.1, 0 ...)

(0.11, 0.3, 0, 0.01 ..)

an essay about sport

another article about politics

Vectors based on word frequencies.

One key issue is to choose the right set of words (or other features)

How did I get these vectors from these two `documents’?

<h1> Compilers</h1>

<p> The Guardian uses several

compilers for its daily cryptic

crosswords. One of the most

frequently used is Araucaria,

and one of the most difficult

is Bunthorne.</p>

<h1> Compilers: lecture 1 </h1>

<p> This lecture will introduce the

concept of lexical analysis, in which

the source code is scanned to reveal

the basic tokens it contains. For this,

we will need the concept of

regular expressions (r.e.s).</p>

26, 2, 2

35, 2, 0

What about these two vectors?

<h1> Compilers</h1>

<p> The Guardian uses several

compilers for its daily cryptic

crosswords. One of the most

frequently used is Araucaria,

and one of the most difficult

is Bunthorne.</p>

<h1> Compilers: lecture 1 </h1>

<p> This lecture will introduce the

concept of lexical analysis, in which

the source code is scanned to reveal

the basic tokens it contains. For this,

we will need the concept of

regular expressions (r.e.s).</p>

1, 1, 1, 0, 0, 0

0, 0, 0, 1, 1, 1

From this MASTER WORD LIST (ordered)

(Crossword, Cryptic, Difficult, Expression, Lexical, Token)

If a document contains `crossword’, it gets a 1 in position 1 of the vector, otherwise 0. If it contains `lexical’, it gets a 1 in position 5, otherwise 0, and so on.

How similar would be the vectors for two docs about

crossword compilers?

.

Turning a document into a vector

We start with a template for the vector, which needs a master list of terms . A term can be a word, or a number, or anything that appears frequently in documents.

There are almost 200,000 words in English – it would take much too

long to process documents vectors of that length.

Commonly, vectors are made from a small number (50—1000) of

most frequently-occurring words.

However, the master list usually does not include words from a stoplist,

Which contains words such as the, and, there, which, etc … why?

The TFIDF Encoding(Term Frequency x Inverse Document Frequency)

A term is a word, or some other frequently occuring item

Given some term i, and a document j, the term count

is the number of times that term i occurs in document j

Given a collection of k terms and a set D of documents, the term frequency, is:

… considering only the terms of interest, this is the proportion of document j that is made up from term i.

The TFIDF Encoding(Term Frequency x Inverse Document Frequency)

A term is a word, or some other frequently occuring item

Given some term i, and a document j, the term count

is the number of times that term i occurs in document j

Given a collection of k terms and a set D of documents, the term frequency, is:

frequency of this word in this doc

total number of words in this doc

… considering only the terms of interest, this is the proportion of document j that is made up from term i.

Term frequency is a measure of

the importance of this term in this document

Inverse document frequency (which we see next) is a measure of the discriminatory valueof the term in the collection of documents we are looking at..

It is a measure of the rarity of this word in this document collection

E.g. high term frequency for “apple” means that apple is an important word in a specific document.

But high document frequency (low inverse document frequency) for “apple”, given a particular set of documents, means that apple does not carry much useful information, since it is in all of the documents.

Inverse document frequency of term i is:

Log of: … the number of documents in the master collection,

divided by the number of those documents that contain the term.

TFIDF encoding of a document

So, given:

- a background collection of documents

(e.g. 100,000 random web pages,

all the articles we can find about cancer

100 student essays submitted as coursework …)

- a specific ordered list (possibly large) of terms

We can encode any document as a vector of TFIDF numbers,

where the ith entry in the vector for document j is:

Turning a document into a vector

Suppose our Master List is:

(banana, cat, dog, fish, read)

Suppose document 1 contains only:

“Bananas are grown in hot countries, and cats like bananas.”

And suppose the background frequencies of these words in a large

random collection of documents is (0.2, 0.1, 0.05, 0.05, 0.2)

The document 1 vector entry for word w is:

This is just a rephrasing of TFIDF, where:

freqindoc(w) is the frequency of w in document 1,

and freq_in_bg(w) is the `background’ frequency in our

reference set of documents

Turning a document into a vector

Master list: (banana, cat, dog, fish, read)

Background frequencies: (0.2, 0.1, 0.05, 0.05, 0.2)

Document 1:

“Bananas are grown in hot countries, and cats like bananas.”

Frequencies are proportions. The background frequency of banana is

0.2, meaning that 20% of documents in general contain `banana’, or bananas, etc. (note that read includes reads, reading, reader, etc…)

The frequency of banana in document 1 is also 0.2 – why?

The TFIDF encoding of this document is:

Suppose another document has

exactly the same vector – will it

be the same document?

0.464, 0.332, 0, 0, 0

Vector representation of documents underpins:

Many areas of automated document analysis

Such as: automated classification of documents

Clustering and organising document collections

Building maps of the web, and of different web communities

Understanding the interactions between different scientific communities, which in turn will lead to helping with automated WWW-based scientific discovery.

Example / recent work of my PhD student Hamouda Chantar

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Hamouda’s work

Focus on automated classification of an article (e.g. Finance, Economics, Sport, Culture, ...)

Emphasis on Feature Selection – which words or other features should constitute the vectors, to enable accurate classification?

We look at 3 pre-classified datasets

Akhbar-Alkhaleej: 5690 Arabic news documents gathered evenly from the online newspaper "Akhbar-Alkhaleej"

Alwatan: 20,291 Arabic news documents gathered from online newspaper "Alwatan”

Al-jazeera-News:1500 documents from the Al-Jazeera news site.

We look at 3 classification methods(when evaluating feature subsets on the test set)

C4.5: well-known decision tree classifier, we use weka’s implementation, “J48”

Naive Bayes: It’s Naive, and it’s Bayes

SVM: with a linear kernel

tara

David Corne, and Nick Taylor, Heriot-Watt University - [email protected]

These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

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