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Data Mining
Goal of Data Mining
Simplification and automation of the overall statistical process, from data source(s) to
model application
Changed over the years
— Replace statistician ? Better models, less grunge work
— Many different data mining algorithms / tools available
— Statistical expertise required to compare different techniques
— Build intelligence into the software
Data Mining Is…
Decision Trees
Nearest Neighbor Classification
Neural Networks
Rule Induction
K-means Clustering
Data Mining is Not...
Data warehousing
SQL / Ad Hoc Queries / Reporting
Software Agents
Online Analytical Processing (OLAP)
Data Visualization
Why data-mining now?
Data mining is an increasingly popular topic
(If the number of new textbooks is anything to go by).
Two main reasons:
With computers now mediating most aspects of our lives, there has been a
large increase in the accumulation of electronic data.
With computers being increasingly up to the demands of complex modeling, it
is getting easier to process larger datasets.
Why Mine Data? Commercial Viewpoint
 Data volumes are too large for classical analysis approaches:
 Large number of records
 High dimensional data
 Leverage organization’s data assets
 Only a small portion of the collected data is ever analyzed
 Data that may never be analyzed continues to be collected, at a great
expense, out of fear that something which may prove important in the
future is missing.
Lots of data is being collected and warehoused
Web data, e-commerce
purchases at department/grocery stores
Bank/Credit Card transactions
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Computers have become cheaper and more powerful
Competitive Pressure is Strong
Provide better, customized services for an edge (e.g. In Customer
Relationship Management)
Scientific Viewpoint
Data collected and stored at enormous speeds (GB/hour)
remote sensors on a satellite
telescopes scanning the skies
micro arrays generating gene expression data
scientific simulations generating terabytes of data
Traditional techniques infeasible for raw data
Data mining may help scientists
In classifying and segmenting data
In Hypothesis Formation
Origins of Data Mining
Draws ideas from machine learning/AI, pattern recognition, statistics, and
database systems
Traditional Techniques may be unsuitable due to
Enormity of data
High dimensionality of data
Heterogeneous, distributed nature of data
Mining Large Data Sets - Motivation
There is often information “hidden” in the data that is not readily evident
Human analysts may take weeks to discover useful information
Much of the data is never analyzed at all
What is Data Mining? ----- Many Definitions
Data processing using sophisticated data search capabilities and statistical
algorithms to discover patterns and correlations in large preexisting databases; a
way to discover new meaning in data.
Non-trivial extraction of implicit, previously unknown and potentially useful
information from data.
Exploration & analysis, by automatic or semi-automatic means, of large quantities
of data in order to discover meaningful patterns.
The process of identifying commercially useful patterns or relationships in
databases or other computer repositories through the use of advanced statistical
tools.
The automated extraction of predictive information from (large) databases.
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A step in the knowledge discovery process consisting of particular algorithms
(methods) that under some acceptable objective, produces a particular
enumeration of patterns (models) over the data.
Data mining is the process of discovering interesting knowledge from large amounts
of data stored either in databases, data warehouses, or other information
repositories.
What is (not) Data Mining?
What is not Data Mining?
Look up phone number in
phone directory
Query a Web search engine
for information about
“Amazon”
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What is Data Mining?
Certain names are more prevalent in certain
US locations (O’Brien, O’Rurke, O’Reilly… in
Boston area)
Group together similar documents returned by
search engine according to their context (e.g.
Amazon rainforest, Amazon.com,)
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Statistics/AI
Machine Learning/
Pattern Recognition
Data Mining
Database
systems
Data Mining Types:
 Predictive data mining: This produces the model of the system described by the
given data. It uses some variables or fields in the data set to predict unknown
or future values of other variables of interest.
 Descriptive data mining: This produces new, nontrivial information based on
the available data set. It focuses on finding patterns describing the data that
can be interpreted by humans.
Defining `data'
By `data', we mean sets of variable values, e.g.,
Annual rainfall in Sussex for the last twenty years;
Age, salary and IQ for all members of Sussex faculty.
Records
Values are organised in combinations called records.
Each record has a particular context, e.g., age, salary and IQ specifically for the
Informatics HoD.
Combinations may also be called vectors (esp. in neural-networks) and data-points
(esp. in statistics).
A single record is a datum.
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Tabulation
Data are often presented in a tabulated form, with one datum per row, and one
variable per column.
NAME
smith
bloggs
bush
...
AGE SALARY IQ
42
29
50
36K
30K
60K
130
140
120
Where data are used for prediction, the to-be-predicted variable normally appears in
the final column (and is often called `class').
Basic data-types
Data may be classified according to the number and character of variables involved.

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Univariate/discrete: one variable with integer/symbolic values.
Univariate/continuous: one variable with real/continuous values.
Multivariate/discrete: more than one variable with integer/symbolic values.
Multivariate/continuous: more than one variable with real/continuous values.
Data Mining Tasks...
Classification [Predictive]
Clustering [Descriptive]
Association Rule Discovery [Descriptive]
Sequential Pattern Discovery [Descriptive]
Regression [Predictive]
Deviation Detection [Predictive]
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Classification: Definition
Given a collection of records (training set )
Each record contains a set of attributes, one of the attributes is the class.
Find a model for class attribute as a function of the values of other attributes.
Goal: previously unseen records should be assigned a class as accurately as
possible.
A test set is used to determine the accuracy of the model. Usually, the given
data set is divided into training and test sets, with training set used to build
the model and test set used to validate it.
Explicit and implicit structure
A dataset is a body of data.
Any dataset has explicit structure, ie., the numbers/values in the records.
Generally, there is also implicit structure.
Data mining is the task of identifying and modeling implicit structure, either as an end
in itself or as a means of obtaining new information.
Example: A-level grades
Dataset containing average A-level grades for the past ten years.
Explicit structure is the mapping between years and average grades.
(Explicit structure = `what you see')
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There is also implicit structure---a gradual increase in values over time. (Average
grades are increasing by approx 3% per year.)
Classification Example
categorical Categorical continuousclass
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
No
7
Yes
Divorced 220K
No
8
No
Single
85K
Yes
9
No
Married
75K
No
10
No
Single
90K
Yes
60K
Refund Marital
Status
Taxable
Income Cheat
No
Single
75K
?
Yes
Married
50K
?
No
Married
150K
?
Yes
Divorced 90K
?
No
Single
40K
?
No
Married
80K
?
10
Training
Set
Test
Set
Learn
Classifier
10
Model
Challenges of Data Mining
Scalability
Dimensionality
Complex and Heterogeneous Data
Data Quality
Data Ownership and Distribution
Privacy Preservation
Streaming Data
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Statistical methods
Case-based reasoning
Neural networks
Decision trees
DM & DW:
Data Warehousing + Data Mining = Increased performance of decision making
process
+
Knowledgeable decision makers
Data Mining Applications
 Data Mining For Financial Data Analysis
 Data Mining For Telecommunications Industry
 Data Mining For The Retail Industry
 Data Mining In Healthcare and Biomedical Research
 Data Mining In Science and Engineering
Reference:
1. Introduction to Data Mining by Tan, Steinbach, Kumar
Data Mini ng: C oncepts and T echniques
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2. Data Mining: Concepts and Techniques: Jiawei Han and Micheline Kamber
3. Kurt Thearling, Ph.D. An Introduction to Data Mining. www.thearling.com
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