Data Mining LECTURE # 01 Introduction to Data Mining

Data Mining LECTURE # 01

Introduction to Data Mining

Motivation: “Necessity is the Motivation: “Necessity is the Mother of Invention”Mother of Invention”

• Data Explosion Problem

1. Automated data collection tools (e.g. web, sensor networks) and

mature database technology lead to tremendous amounts of data

stored in databases, data warehouses and other information repositories.

2. Currently enterprises are facing data explosion problem.

• Electronic Information an Important Asset for Business

Decisions

1. With the growth of electronic information, enterprises began to

realizing that the accumulated information can be an important

asset in their business decisions.

2. There is a potential business intelligence hidden in the large volume

of data.

3. This intelligence can be the secret weapon on which the success of a

business may depend.

1. It is not a Simple Matter to discover Business

Intelligence from Mountain of Accumulated Data.

2. What is required are Techniques that allow the

enterprise to Extract the Most Valuable Information.

3. The Field of Data Mining provides such Techniques.

4. These techniques can Find Novel Patterns (unknown)

that may Assist an Enterprise in Understanding the

business better and in forecasting.

Extracting Business Intelligence Extracting Business Intelligence (Solution)(Solution)

Data Mining vs SQL, EIS, and OLAP

4

•SQL. SQL is a query language, difficult for business people to use

•EIS = Executive Information Systems. EIS systems provide graphical interfaces that give executives a pre-programmed (and therefore limited) selection of reports, automatically generating the necessary SQL for each.

•OLAP allows views along multiple dimensions, and drill-drown, therefore giving access to a vast array of analyses. However, it requires manual navigation through scores of reports, requiring the user to notice interesting patterns themselves.

•Data Mining picks out interesting patterns. The user can then use visualization tools to investigate further.

An Example of OLAP Analysis and its Limits

Walking Sticks Sales by City

50

10

400

Karachi

Lahore

Islamabad

Walking Sticks Sales in Islamabad by Age

10 30

360

Less than 20

20 to 60

Older than 60

5

• What is driving sales of walking sticks ?

• Step 1: View some OLAP graphs: e.g. walking stick sales by city.

• Step 2: Noticing that Islamabad has high salesyou decide to investigate further.

• (Before OLAP, you would have to have written a very complex SQL query instead of just simply clicking to drill-down).

• It seems that old people are responsible for most walking stick sales. You confirm this by viewing a chart of age distributions by state.

• But imagine if you had to do this manual investigation for all of the 10,000 products in your range ! Here, OLAP gives way to Data Mining.

Step 2

Step 1

Age Distribution by City

0

20

40

60

80

Karachi Lahore Islamabad

Younger than 20

20 to 60

Older than 60

Data Mining vs Expert Systems

6

•Expert Systems = Rule-Driven DeductionTop-down: From known rules (expertise) and data to decisions.

•Data Mining = Data-Driven InductionBottom-up: From data about past decisions to discovered rules (general rules induced from the data).

Expert System

Data Mining

Rules

Data

RulesData(including past decisions)

Decisions

Difference b/w Machine Learning Difference b/w Machine Learning and Data Miningand Data Mining

• Machine Learning techniques are designed to deal with a

limited amount of artificial intelligence data. Where the Data

Mining Techniques deal with large amount of databases data.

• Data Mining (Knowledge Discovery in

Databases) – Extraction of interesting (non-trivial, implicit, previously

unknown and potentially useful) information or patterns from data in large databases.

• What is not Data Mining?– (Deductive) query processing. – Expert systems or small ML/statistical programs

Data Mining (Example)Data Mining (Example)• Random Guessing vs. Potential Knowledge

– Suppose we have to Forecast the Probability of Rain in

Islamabad city for any particular day.

– Without any Prior Knowledge the probability of rain would be

50% (pure random guess).

– If we had a lot of weather data, then we can extract

potential rules using Data Mining which can then forecast

the chance of rain better than random guessing.

• Example: The Rule

if [Temperature = ‘hot’ and Humidity = ‘high’] then there is

66.6% chance of rain. Temperature Humidity Windy Rainhot high false Nohot high true Yeshot high false Yesmild high false Nocool normal false Nocool normal true Yes

The Data Mining Process

9

• Step 0: Determine Business Objective- e.g. Forecasting the probability of rain

- Must have relevant prior knowledge and goals of application.

• Step 1: Prepare Data- Noisy and Missing values handling (Data Cleaning).- Data Transformation (Normalization/Discretization).- Attribute/Feature Selection.

• Step 2: Choosing the Function of Data Mining- Classification, Clustering, Association Rules

• Step 3: Choosing The Mining Algorithm- Selection of correct algorithm depending upon the quality of

data.- Selection of correct algorithm depending upon the density

of data.

• Step 4: Data Mining- Search patterns of interest:- A typical data mining algorithm

can mine millions of patterns.

• Step 5: Visualization/Knowledge Representation - Visualization/Representation of interesting patterns,

etc

Data Mining: A KDD Process

– Data mining: the core of knowledge discovery process.

Data Cleaning

Data Integration

Databases

Data Warehouse

Task-relevant Data

Data Mining

Pattern Evaluation

Data Mining: On What Kind of Data?

1. Relational databases

2. Data warehouses

3. Transactional databases

4. Advanced DB and information repositories– Time-series data and temporal data– Text databases – Multimedia databases– Data Stream (Sensor Networks Data)– WWW

Data Mining Functionalities (1)

• Data Preprocessing– Handling Missing and Noisy Data (Data Cleaning).

– Techniques we will cover.• Missing values Imputation using Mean, Median and Mod.

• Missing values Imputation using K-Nearest Neighbor.

• Missing values Imputation using Association Rules Mining.

• Data Binning for Noisy Data.

TID Refund Country Taxable Income Cheat

1 Yes USA 125K No

2 UK 100K No

3 No Australia 70K No

4 120K No

5 No NZL 95K Yes

Data Mining Functionalities (1)• Data Preprocessing

– Data Transformation (Discretization and Normalization).

– With the help of data transformation rules become more General and Compact.

– General and Compact rules increase the Accuracy of Classification.

Age

Child

Child

Young

Young

Old

Old

Child

Young

Child = (0 to 20)

Young = (21 to 47)

Old = (48 to 120)

Age

15

18

40

33

55

48

12

23

1. If attribute 1 = value1 & attribute 2 = value2 and Age = 08 then Buy_Computer = No.



1. If attribute 1 = value1 & attribute 2 = value2 and Age = Child then Buy_Computer = No.


– Attribute Selection/Feature Selection

• Selection of those attributes which are more relevant to data mining task.

• Advantage1: Decrease the processing time of mining task.

• Advantage2: Generalize the rules.

– Example

• If our mining goal is to find that countries which has more Cheat on which Taxable Income.

• Then obviously the date attribute will not be an important factor in our mining task.

Date Refund Country Taxable Income Cheat

11/02/2002

Yes USA 125K No

13/02/2002

Yes UK 100K No

16/02/2002

No Australia 120K Yes

21/03/2002

No Australia 120K Yes

26/02/2002

No NZL 95K Yes


• Principle Component Analysis

• Wrapper Based

• Filter Based

Data Mining Functionalities (2)• Association Rule Mining • In Association Rule Mining Framework we have to find all

the rules in a transactional/relational dataset which contain a support (frequency) Greater than some minimum support (min_sup) threshold (provided by the user).

• For example with min_sup = 50%.

Transaction ID Items Bought2000 Bread,Butter,Egg1000 Bread,Butter, Egg4000 Bread,Butter, Tea5000 Butter, Ice cream, Cake

Itemset Support{Butter} 4{Bread} 3{Egg} 2

{Bread,Butter} 3{Bread, Butter, Egg} 2

Data Mining Functionalities (2)• Association Rule Mining • Topic we will cover

– Frequent Itemset Mining Algorithms (Apriori, FP-Growth, Bit-vector ).

– Fault-Tolerant/Approximate Frequent Itemset Mining.– N-Most Interesting Frequent Itemset Mining.– Closed and Maximal Frequent Itemset Mining.– Incremental Frequent Itemset Mining– Sequential Patterns.

Data Mining Functionalities (2)• Classification and Prediction

– Finding models (functions) that describe and distinguish classes or concepts for future prediction

– Example: Classify rainy/un-rainy cities based on Temperature, Humidify and Windy Attributes.

– Must have known the previous business decisions (Supervised Learning).

City Temperature Humidity Windy RainLahore hot low false NoIslamabad hot high true YesIslamabad hot high false YesMultan mild low false NoKarachi cool normal false NoRawalpindi hot high true Yes

Rule

• If Temperature = Hot & Humidity = High then Rain = Yes.

City Temperature Humidity Windy RainMuree hot high false ?Sibi mild low true ?

Prediction of unknown record

Data Mining Functionalities (2)• Cluster Analysis

– Group data to form new classes based on un-labels class data.

– Business decisions are unknown (Also called unsupervised Learning).

– Example: Classify rainy/un-rainy cities based on Temperature, Humidify and Windy Attributes.

City Temperature Humidity Windy RainLahore hot low false ?Islamabad hot high true ?Islamabad hot high false ?Multan mild low false ?Karachi cool normal false ?Rawalpindi hot high true ?

3 clusters

Data Mining Functionalities (3)• Outlier Analysis

– Outlier: A data object that does not comply with the general

behavior of the data.

– It can be considered as noise or exception but is quite useful in

fraud detection, rare events analysis

City Temperature Humidity Windy RainLahore hot low false ?Islamabad hot high true ?Islamabad hot high false ?Multan mild low false ?Karachi cool normal false ?Rawalpindi hot high true ?

2 outliers

Are All the “Discovered” Patterns Interesting?

• A data mining system/query may generate

thousands of patterns, not all of them are

interesting.

– Suggested approach: Query-based,

Constraint mining

• Interestingness Measures: A pattern is

interesting if it is easily understood by humans,

valid on new or test data with some degree of

certainty, potentially useful, novel, or validates

some hypothesis that a user seeks to confirm

Can We Find All and Only Interesting Patterns?

• Find all the interesting patterns: Completeness– Can a data mining system find all the interesting patterns?

– Remember most of the problems in Data Mining are NP-Complete.

– There is no global best solution for any single problem.

• Search for only interesting patterns: Optimization– Can a data mining system find only the interesting patterns?

– Approaches

• First general all the patterns and then filter out the uninteresting ones.

• Generate only the interesting patterns—Constraint based mining (Give threshold factors in mining)

Reading Assignment• Book Chapter

– Chapter 1 of “Jiawei Han and Micheline Kamber” book “Data Mining: Concepts and Techniques”.

• Some Nice Resources– ACM Special Interest Group on Knowledge Discovery and

Data Mining (SIGKDD) http://www.acm.org/sigs/sigkdd/.

– Knowledge Discovery Nuggets www.kdnuggests.com.– IEEE Transactions on Knowledge and Data Engineering –

http://www.computer.org/tkde/.

– IEEE Transactions on Pattern Analysis and Machine Intelligence – http://www.computer.org/tpami/.

– Data Mining and Knowledge Discovery - Publisher: Springer Science+Business Media B.V., Formerly Kluwer Academic Publishers B.V. http://www.kluweronline.com/issn/1384-5810/. current and previous offerings of Data Mining course at Stanford, CMU, MIT and Helsinki.

Data Mining ------- Where?

Text and Reference Material• The course will be mainly based on research

literature, following text may however be consulted:

– Jiawei Han and Micheline Kamber. “Data Mining: Concepts and Techniques”.

1. David Hand, Heikki Mannila and Padhraic Smyth. “Principles of Data Mining”. Pub. Prentice Hall of India, 2004.

2. Sushmita Mitra and Tinku Acharya. “Data Mining: Multimedia, Soft Computing and Bioinformatics”. Pub. Wiley an Sons Inc. 2003.

3. Usama M. Fayyad et al. “Advances in Knowledge Discovery and Data Mining”, The MIT Press, 1996.

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Data Mining LECTURE # 01 Introduction to Data Mining