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Applying Data Mining

By Susan L. Miertschin

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Is Data Mining Appropriate for the

ro em a an

Can you clearly define the problem?

Does potentially meaningful data exist? Does the data contain hidden knowledge or is the data factual

an use u or reporting purpose on y

Will the cost of processing the data be less than the likely

gained from the data mining project?

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Determine if the Problem is Suitable for

Shallow Knowledge

Mu ti imensiona Know e ge

Hidden Knowledge

Deep Knowledge

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our ypes o now e e

Factual

Factual

On-line analytical Processing

manipulated in a database

oo s use o man pu a emultidimensional knowledge

Not easily found using database query

Data mining algorithms can findpatterns

Can only be found if some

direction about what we are

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Data Mining vs. Data Query: An

xamp e

You already almost know Find regularities in data

obvious without the aid of

tools y

Amount of data

Organization of data

o scures patterns Limits of human capabilities

to consider many things at

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A computer program that

-

A person trained to

solving skills of one or

more human experts

order to capture the

experts implicitknowledge in explicit form

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Data Mining ToolData

If Swollen Glands = YesThen Diagnosis = Strep Throat

Expert SystemBuilding Tool

Human Expert Knowledge Engineer

If Swollen Glands = Yes

Figure 1.2 Data mining vs. expert systems

Then Diagnosis = Strep Throat

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What is simple search? Nearest neighbor classifier

K-nearest neighbor classifier

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Create a table of instances with known classifications

is is t e training ata

Get a new instance

using the Euclidean distance metric for comparison

purposes 22

Find the instance in the training set that is closest on thebasis of the distance metric to the new instant

11 nn

Classify the new instance the same way as the one closest toit in the training data

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Problems with Nearest Neighbor

ass ca on

Computation times will be large when the training set is

large

No differentiation of relevant from irrelevant attributes

o way to te w ic attri utes i erentiate among c asses

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Different algorithms are available for different data mining tasks

Different tools exist that implement different algorithms and

different versions of algorithms

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e.g., Algorithms Available in Microsofts

na ys s erv ces

Decision Trees

Linear Regression Nave Bayes

Clustering Algorithms

Association Rules Sequence Clustering

Time Series Analysis

Neura Networ s Logistic Regression

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Applying Data Mining

By Susan L. Miertschin

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