An introduction to data mining

DATA MINING
Introduction
Data mining (DM), also called Knowledge-Discovery in Databases (KDD) or
Knowledge-Discovery and Data Mining, is the process of automatically searching
large volumes of data for patterns such as association rules. It is a fairly recent topic
in computer science but applies many older computational techniques from statistics,
information retrieval, machine learning and pattern recognition.
Data mining, the extraction of hidden predictive information from large
databases, is a powerful new technology with great potential to help companies focus
on the most important information in their data warehouses. Data mining tools predict
future trends and behaviors, allowing businesses to make proactive, knowledge-driven
decisions. The automated, prospective analyses offered by data mining move beyond
the analyses of past events provided by retrospective tools typical of decision support
systems. Data mining tools can answer business questions that traditionally were too
time consuming to resolve. They scour databases for hidden patterns, finding
predictive information that experts may miss because it lies outside their
expectations.Most companies already collect and refine massive quantities of data.
Data mining techniques can be implemented rapidly on existing software and
hardware platforms to enhance the value of existing information resources, and can be
integrated with new products and systems as they are brought on-line.
The Foundations of Data Mining
Data mining techniques are the result of a long process of research and product
development. This evolution began when business data was first stored on computers,
continued with improvements in data access, and more recently, generated
technologies that allow users to navigate through their data in real time. Data mining
takes this evolutionary process beyond retrospective data access and navigation to
prospective and proactive information delivery. Data mining is ready for application
in the business community because it is supported by three technologies that are now
sufficiently mature:
1

1. Massive data collection
2. Powerful multiprocessor computers
3. Data mining algorithms
Commercial databases are growing at unprecedented rates. A recent META Group
survey of data warehouse projects found that 19% of respondents are beyond the 50
gigabyte level, while 59% expect to be there by second quarter of 1996.1 In some
industries, such as retail, these numbers can be much larger. The accompanying need
for improved computational engines can now be met in a cost-effective manner with
parallel multiprocessor computer technology. Data mining algorithms embody
techniques that have existed for at least 10 years, but have only recently been
implemented as mature, reliable, understandable tools that consistently outperform
older statistical methods.
In the evolution from business data to business information, each new step has
built upon the previous one. For example, dynamic data access is critical for drill-
through in data navigation applications, and the ability to store large databases is
critical to data mining. From the user’s point of view, the four steps listed in Table 1
were revolutionary because they allowed new business questions to be answered
accurately and quickly.
Evolutionary
Step
Business Question Enabling
Technologies
Product
Providers
Characteristics
Data Collection
(1960s)
"What was my total
revenue in the last five
years?"
Computers, tapes,
disks
IBM, CDC Retrospective,
static data
delivery
Data Access
(1980s)
"What were unit sales in
New England last
March?"
Relational databases
(RDBMS),
Structured Query
Language (SQL),
ODBC
Oracle,
Sybase,
Informix,
IBM,
Microsoft
Retrospective,
dynamic data
delivery at
record level
Data
Warehousing &
"What were unit sales in
New England last
On-line analytic
processing (OLAP),
Pilot,
Comshare,
Retrospective,
dynamic data
2

Decision Support
(1990s)
March? Drill down to
Boston."
multidimensional
databases, data
warehouses
Arbor,
Cognos,
Microstrategy
delivery at
multiple levels
Data Mining
(Emerging
Today)
"What’s likely to
happen to Boston unit
sales next month?
Why?"
Advanced
algorithms,
multiprocessor
computers, massive
databases
Pilot,
Lockheed,
IBM, SGI,
numerous
startups
(nascent
industry)
Prospective,
proactive
information
delivery
Table 1. Steps in the Evolution of Data Mining.
The core components of data mining technology have been under development
for decades, in research areas such as statistics, artificial intelligence, and machine
learning. Today, the maturity of these techniques, coupled with high-performance
relational database engines and broad data integration efforts, make these
technologies practical for current data warehouse environments.
The Scope of Data Mining
Data mining derives its name from the similarities between searching for valuable
business information in a large database — for example, finding linked products in
gigabytes of store scanner data — and mining a mountain for a vein of valuable ore.
Both processes require either sifting through an immense amount of material, or
intelligently probing it to find exactly where the value resides. Given databases of
sufficient size and quality, data mining technology can generate new business
opportunities by providing these capabilities:
Automated prediction of trends and behaviors. Data mining automates the
process of finding predictive information in large databases. Questions that
traditionally required extensive hands-on analysis can now be answered directly
from the data — quickly. A typical example of a predictive problem is targeted
marketing. Data mining uses data on past promotional mailings to identify the
3

targets most likely to maximize return on investment in future mailings. Other
predictive problems include forecasting bankruptcy and other forms of default,
and identifying segments of a population likely to respond similarly to given
events.
Automated discovery of previously unknown patterns. Data mining tools
sweep through databases and identify previously hidden patterns in one step. An
example of pattern discovery is the analysis of retail sales data to identify
seemingly unrelated products that are often purchased together. Other pattern
discovery problems include detecting fraudulent credit card transactions and
identifying anomalous data that could represent data entry keying errors.
Data mining techniques can yield the benefits of automation on existing software and
hardware platforms, and can be implemented on new systems as existing platforms
are upgraded and new products developed. When data mining tools are implemented
on high performance parallel processing systems, they can analyze massive databases
in minutes. Faster processing means that users can automatically experiment with
more models to understand complex data. High speed makes it practical for users to
analyze huge quantities of data. Larger databases, in turn, yield improved predictions.
Databases can be larger in both depth and breadth:
More columns. Analysts must often limit the number of variables they examine
when doing hands-on analysis due to time constraints. Yet variables that are
discarded because they seem unimportant may carry information about unknown
patterns. High performance data mining allows users to explore the full depth of a
database, without preselecting a subset of variables.
More rows. Larger samples yield lower estimation errors and variance, and allow
users to make inferences about small but important segments of a population.
A recent Gartner Group Advanced Technology Research Note listed data mining
and artificial intelligence at the top of the five key technology areas that "will clearly
have a major impact across a wide range of industries within the next 3 to 5 years."2
Gartner also listed parallel architectures and data mining as two of the top 10 new
4

technologies in which companies will invest during the next 5 years. According to a
recent Gartner HPC Research Note, "With the rapid advance in data capture,
transmission and storage, large-systems users will increasingly need to implement
new and innovative ways to mine the after-market value of their vast stores of detail
data, employing MPP [massively parallel processing] systems to create new sources
of business advantage (0.9 probability)."
The most commonly used techniques in data mining are:
1. Artificial neural networks: Non-linear predictive models that learn
through training and resemble biological neural networks in structure.
2. Decision trees: Tree-shaped structures that represent sets of decisions.
These decisions generate rules for the classification of a dataset. Specific
decision tree methods include Classification and Regression Trees (CART)
and Chi Square Automatic Interaction Detection (CHAID) .
3. Genetic algorithms: Optimization techniques that use processes such as
genetic combination, mutation, and natural selection in a design based on
the concepts of evolution.
4. Nearest neighbor method: A technique that classifies each record in a
dataset based on a combination of the classes of the k record(s) most
similar to it in a historical dataset (where k ³ 1). Sometimes called the k-
nearest neighbor technique.
5. Rule induction: The extraction of useful if-then rules from data based on
statistical significance.
Many of these technologies have been in use for more than a decade in
specialized analysis tools that work with relatively small volumes of data. These
capabilities are now evolving to integrate directly with industry-standard data
warehouse and OLAP platforms. The appendix to this white paper provides a glossary
of data mining terms.
How Data Mining Works
How exactly is data mining able to tell you important things that you didn't
know or what is going to happen next? The technique that is used to perform these
5

feats in data mining is called modeling. Modeling is simply the act of building a
model in one situation where you know the answer and then applying it to another
situation that you don't. For instance, if you were looking for a sunken Spanish
galleon on the high seas the first thing you might do is to research the times when
Spanish treasure had been found by others in the past. You might note that these ships
often tend to be found off the coast of Bermuda and that there are certain
characteristics to the ocean currents, and certain routes that have likely been taken by
the ship’s captains in that era. You note these similarities and build a model that
includes the characteristics that are common to the locations of these sunken
treasures. With these models in hand you sail off looking for treasure where your
model indicates it most likely might be given a similar situation in the past.
Hopefully, if you've got a good model, you find your treasure.
This act of model building is thus something that people have been doing for a
long time, certainly before the advent of computers or data mining technology. What
happens on computers, however, is not much different than the way people build
models. Computers are loaded up with lots of information about a variety of situations
where an answer is known and then the data mining software on the computer must
run through that data and distill the characteristics of the data that should go into the
model. Once the model is built it can then be used in similar situations where you
don't know the answer. For example, say that you are the director of marketing for a
telecommunications company and you'd like to acquire some new long distance phone
customers. You could just randomly go out and mail coupons to the general
population - just as you could randomly sail the seas looking for sunken treasure. In
neither case would you achieve the results you desired and of course you have the
opportunity to do much better than random - you could use your business experience
stored in your database to build a model.
As the marketing director you have access to a lot of information about all of
your customers: their age, sex, credit history and long distance calling usage. The
good news is that you also have a lot of information about your prospective
customers: their age, sex, credit history etc. Your problem is that you don't know the
long distance calling usage of these prospects (since they are most likely now
customers of your competition). You'd like to concentrate on those prospects who
6

have large amounts of long distance usage. You can accomplish this by building a
model. Table 2 illustrates the data used for building a model for new customer
prospecting in a data warehouse.
Customers Prospects
General information (e.g. demographic
data)
Known Known
Proprietary information (e.g. customer
transactions)
Known Target
Table 2 - Data Mining for Prospecting
The goal in prospecting is to make some calculated guesses about the
information in the lower right hand quadrant based on the model that we build going
from Customer General Information to Customer Proprietary Information. For
instance, a simple model for a telecommunications company might be:
98% of my customers who make more than $60,000/year spend more than $80/month
on long distance
This model could then be applied to the prospect data to try to tell something
about the proprietary information that this telecommunications company does not
currently have access to. With this model in hand new customers can be selectively
targeted.
Test marketing is an excellent source of data for this kind of modeling. Mining
the results of a test market representing a broad but relatively small sample of
prospects can provide a foundation for identifying good prospects in the overall
market. Table 3 shows another common scenario for building models: predict what is
going to happen in the future.
7

Yesterday Today Tomorrow
Static information and current plans
(e.g. demographic data, marketing
plans)
Known Known Known
Dynamic information (e.g.
customer transactions)
Known Known Target
Table 3 - Data Mining for Predictions
If someone told you that he had a model that could predict customer usage
how would you know if he really had a good model? The first thing you might try
would be to ask him to apply his model to your customer base - where you already
knew the answer. With data mining, the best way to accomplish this is by setting
aside some of your data in a vault to isolate it from the mining process. Once the
mining is complete, the results can be tested against the data held in the vault to
confirm the model’s validity. If the model works, its observations should hold for the
vaulted data.
An Architecture for Data Mining
To best apply these advanced techniques, they must be fully integrated with a
data warehouse as well as flexible interactive business analysis tools. Many data
mining tools currently operate outside of the warehouse, requiring extra steps for
extracting, importing, and analyzing the data. Furthermore, when new insights require
operational implementation, integration with the warehouse simplifies the application
of results from data mining. The resulting analytic data warehouse can be applied to
improve business processes throughout the organization, in areas such as promotional
campaign management, fraud detection, new product rollout, and so on. Figure 1
illustrates an architecture for advanced analysis in a large data warehouse.
8

Figure 1 - Integrated Data Mining Architecture
The ideal starting point is a data warehouse containing a combination of
internal data tracking all customer contact coupled with external market data about
competitor activity. Background information on potential customers also provides an
excellent basis for prospecting. This warehouse can be implemented in a variety of
relational database systems: Sybase, Oracle, Redbrick, and so on, and should be
optimized for flexible and fast data access.
An OLAP (On-Line Analytical Processing) server enables a more
sophisticated end-user business model to be applied when navigating the data
warehouse. The multidimensional structures allow the user to analyze the data as they
want to view their business – summarizing by product line, region, and other key
perspectives of their business. The Data Mining Server must be integrated with the
data warehouse and the OLAP server to embed ROI-focused business analysis
directly into this infrastructure. An advanced, process-centric metadata template
defines the data mining objectives for specific business issues like campaign
management, prospecting, and promotion optimization. Integration with the data
warehouse enables operational decisions to be directly implemented and tracked. As
the warehouse grows with new decisions and results, the organization can continually
mine the best practices and apply them to future decisions.
9

This design represents a fundamental shift from conventional decision support
systems. Rather than simply delivering data to the end user through query and
reporting software, the Advanced Analysis Server applies users’ business models
directly to the warehouse and returns a proactive analysis of the most relevant
information. These results enhance the metadata in the OLAP Server by providing a
dynamic metadata layer that represents a distilled view of the data. Reporting,
visualization, and other analysis tools can then be applied to plan future actions and
confirm the impact of those plans.
Profitable Applications
A wide range of companies have deployed successful applications of data
mining. While early adopters of this technology have tended to be in information-
intensive industries such as financial services and direct mail marketing, the
technology is applicable to any company looking to leverage a large data warehouse
to better manage their customer relationships. Two critical factors for success with
data mining are: a large, well-integrated data warehouse and a well-defined
understanding of the business process within which data mining is to be applied (such
as customer prospecting, retention, campaign management, and so on).
Some successful application areas include:
1. A pharmaceutical company can analyze its recent sales force activity and
their results to improve targeting of high-value physicians and determine
which marketing activities will have the greatest impact in the next few
months. The data needs to include competitor market activity as well as
information about the local health care systems. The results can be
distributed to the sales force via a wide-area network that enables the
representatives to review the recommendations from the perspective of the
key attributes in the decision process. The ongoing, dynamic analysis of
the data warehouse allows best practices from throughout the organization
to be applied in specific sales situations.
2. A credit card company can leverage its vast warehouse of customer
transaction data to identify customers most likely to be interested in a new
credit product. Using a small test mailing, the attributes of customers with
10

an affinity for the product can be identified. Recent projects have indicated
more than a 20-fold decrease in costs for targeted mailing campaigns over
conventional approaches.
3. A diversified transportation company with a large direct sales force can
apply data mining to identify the best prospects for its services. Using data
mining to analyze its own customer experience, this company can build a
unique segmentation identifying the attributes of high-value prospects.
Applying this segmentation to a general business database such as those
provided by Dun & Bradstreet can yield a prioritized list of prospects by
region.
4. A large consumer package goods company can apply data mining to
improve its sales process to retailers. Data from consumer panels,
shipments, and competitor activity can be applied to understand the
reasons for brand and store switching. Through this analysis, the
manufacturer can select promotional strategies that best reach their target
customer segments.
Each of these examples have a clear common ground. They leverage the knowledge
about customers implicit in a data warehouse to reduce costs and improve the value of
customer relationships. These organizations can now focus their efforts on the most
important (profitable) customers and prospects, and design targeted marketing
strategies to best reach them.
Data Mining Technologies
Data Mining Technologies provides next generation decision support software and
services for Data Mining and Business Intelligence applications for business and
science. Some samples:
1. Internet e-Commerce
2. Direct Marketing
3. Healthcare
4. Pharmaceuticals
5. Genetics
11

6. CRM
7. Telecommunications
8. Utilities
9. Financial Services
Solutions :
Suite Of Nuggets Business And Scientific Solutions Are Based On Our Unique And
New Sift Agent technology. "Sift Agent technology" is the proprietary method used
by data mining software to extract English "if-then" rules from files.
Features of the Sift Agent Technology are:
1. Easy to use
2. Low cost of ownership
3. Understandable results (not a black box)
4. Produces more accurate results
5. Faster without using expensive hardware
6. No need for a high level of statistical skill
7. Finds small patterns other methods miss
8. Handles up to 50,000 variables without clustering
The results produced by software are tailored especially for the decision maker as
well as the technician, so that data mining projects can be done by the end user as well
as the specialist.
Data Mining Tools
See5 and C5.0
12

Windows 98/Me/2000/XP and its Unix counterpart C5.0 are sophisticated data
mining tools for discovering patterns that delineate categories, assembling them into
classifiers, and using them to make predictions.
Some important features:
1. See5/C5.0 has been designed to analyse substantial databases containing
thousands to hundreds of thousands of records and tens to hundreds of
numeric, time, date, or nominal fields.
2. To maximize interpretability, See5/C5.0 classifiers are expressed as decision
trees or sets of if-then rules, forms that are generally easier to understand
than neural networks.
3. See5/C5.0 is easy to use and does not presume any special knowledge of
Statistics or Machine Learning (although these don't hurt, either!)
4. RuleQuest provides C source code so that classifiers constructed by
See5/C5.0 can be embedded in your organization's own systems.
Cubist
Cubist builds rule-based predictive models that output values, complementing
See5/C5.0 that predicts categories. For instance, See5/C5.0 might classify the
percentage yield from some process as "high", "medium", or "low", whereas Cubist
would output a number such as "73".
Cubist is a powerful tool for generating rule-based models that balance the need for
accurate prediction against the requirements of intelligibility. Cubist models generally
give better results than those produced by simple techniques such as multivariate
linear regression, while also being easier to understand than neural networks.
13

1. Cubist has been designed to analyze substantial databases containing
hundreds of thousands of records and tens to thousands of numeric or nominal
fields. If you have used neural networks or similar modeling tools, you'll be
surprised by Cubist's speed! (Cubist also takes advantage of processors with
dual cores, dual CPUs, or Intel Hyper-Threading to speed up model-building.)
2. To maximize interpretability, Cubist models are expressed as collections of
rules, where each rule has an associated multivariate linear model.
Whenever a situation matches a rule's conditions, the associated model is used
to calculate the predicted value.
3. Cubist is available for Windows 98/Me/2000/XP and several flavors of Unix.
4. Cubist is easy to use and does not presume advanced knowledge of Statistics
or Machine Learning (although these don't hurt, either!)
5. RuleQuest provides C source code so that models constructed by Cubist can
be embedded in your organization's own systems.
Magnum Opus
Magnum Opus delivers the power of the most recent advances in pattern discovery
technology. With unrivalled flexibility and ease-of-use, it finds new and
unanticipated patterns from data. Use Magnum Opus to unlock the wealth of
information implicit within your data.
1. Magnum Opus is easy to use and does not presume advanced knowledge of
statistics or machine learning.
2. Magnum Opus patterns are expressed in forms that are simple to
understand.
3. Magnum Opus includes a unique facility to automatically filter out spurious
'discoveries'.
4. Unlike other pattern discovery systems, Magnum Opus does not rely on
sparse data for efficient processing.
5. Magnum Opus finds patterns from both transaction data and attribute-value
data.
14

6. Magnum Opus has been designed to analyze substantial databases
containing millions of records.
7. Magnum Opus is fast. It has linear compute time. If the amount of data is
doubled then the compute time will approximately double†
.
8. Magnum Opus is flexible. The different search options support a wide range
of differing analytic objectives.
GritBot
GritBot is an automatic tool that tries to find anomalies in data as a precursor to data
mining. It can be thought of as an autonomous data quality auditor that hunts for
records having "surprising" values of nominal (discrete) and/or numeric (continuous)
attributes.
1. GritBot has been designed to analyze substantial databases containing tens
or hundreds of thousands of records and many numeric or nominal fields.
2. Possibly anomalous values that GritBot identifies are reported, together with
an explanation of why each value seems surprising.
3. The patterns found by GritBot can be saved and used to check new data.
Potential anomalies found in new data can differ from the types of anomalies
originally identified.
4. GritBot is virtually automatic -- the user does not require a knowledge of
Statistics or Data Analysis.
5. GritBot is available for Windows 98/Me/2000/XP, Linux, and Solaris.
ODBCHook
ODBCHook is a Windows application that extracts data from ODBC sources (e.g.
Microsoft Excel and Access) and builds data files that can be used with See5/C5.0,
Cubist, or GritBot.
There are two prerequisites for using ODBCHook:
15

1. Your data must be available on some ODBC-compliant data source and
you must have all the necessary access permissions, passwords etc. The
ODBC driver for the relevant ODBC source must have been installed on
your machine.
2. You must have prepared a .names file that describes the fields of interest in
your dataset
some reasons why your data mining approach is better than statistics?
It handles more variables (up to 50,000 in many cases), does not inject noise
into the input data by requiring you to fill in missing data with assumed values, and it
allows for small patterns to be recognized and not smoothed out.
Conclusion
16

Data mining, the extraction of hidden predictive information from large databases, is a
powerful new technology with great potential to help companies focus on the most
important information in their data warehouses. Comprehensive data warehouses that
integrate operational data with customer, supplier, and market information have
resulted in an explosion of information. Competition requires timely and sophisticated
analysis on an integrated view of the data.
17

An introduction to data mining

More Related Content

What's hot (20)

Viewers also liked (9)

Similar to An introduction to data mining (20)

More from Shiva Krishna Chandra Shekar (20)

Recently uploaded (20)

An introduction to data mining