Download Data Mining Data Mining – Task Types Data Mining

3/24/2011
Data Mining
By Susan Miertschin
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Data Mining – Task Types
Data mining is useful for certain types of tasks
As new algorithms are developed and evolve, new task types or
extensions of existing task types may evolve
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Data Mining - Task Types
 Classification
 Clustering
 Discovering Association Rules
 Discovering Sequential Patterns – Sequence Analysis
 Regression
 Detecting Deviations from Normal
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Prediction versus Description
 The purpose of some tasks is to describe the status quo
 The purpose of some tasks is be able to predict something
based on something else
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Supervised versus Unsupervised
 The purpose of some tasks is to describe the status quo
 Techniques in this category are referred to as unsupervised
 The purpose of some tasks is be able to predict something
based on something else
 Techniques in this category are referred to as supervised
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Description and Examples
Data Mining - Task Types
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Data Mining - Task Types
 Classification
 Fit items into slots – Larson
 Assign items in a collection to target categories or classes –
Oracle book (http://download.oracle.com/docs/cd/B28359_01/datamine.111/b28129/classify.htm)
 Clustering
 Discovering Association Rules
 Discovering Sequential Patterns – Sequence Analysis
 Regression
 Detective Deviations from Normal
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Classification
 Here is data about loan applicants. Which are good risks and
which are poor risks?
 Which of our current customers are likely to increase their
current rate of spending if given an affinity card?
 Which consumers are likely to buy a new cell-phone
cell phone product
if I send them a direct mailing?
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Classification
Predictive or Descriptive?
Supervised or Unsupervised?
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Classification
Predictive
Supervised
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Classification – Predictive - Supervised
 Predict which loan applicants are good risks and which are
poor risks
 Predict which of our current customers are likely to increase
their current rate of spending if given an affinity card
 Predict which consumers are likely to buy a new cell-phone
cell phone
product if I send them a direct mailing
 Predict which patients will respond better to treatment A or
treatment B
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Data Mining - Task Types
 Classification
 Clustering
 Divide data into groups with similar characteristics - Larson
 Find clusters of data objects similar in some way to one another
– Oracle book
(http://download oracle com/docs/cd/B28359 01/datamine 111/b28129/clustering htm)
(http://download.oracle.com/docs/cd/B28359_01/datamine.111/b28129/clustering.htm)
 Discovering Association Rules
 Discovering Sequential Patterns – Sequence Analysis
 Regression
 Detective Deviations from Normal
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Clustering
 Find customers similar to each other based on geographical
distance to nearest store-front location, number of small
dogs owned, number of cats owned, and number of children
in household
 Purpose?
p
Target
g niche markets,, pplan new stores
 Find cardiologists who are similar with respect to likelihood
of prescribing a certain class of medication for treatment of
congestive heart failure (based on hospital patient records)
and patient mix demographics
 Purpose? Target these cardiologists for a particular marketing
effort related to a new pharmaceutical product
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Clustering
Predictive or Descriptive?
Supervised or Unsupervised?
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Clustering
Descriptive
Unsupervised
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Data Mining - Task Types
 Classification
 Clustering
 Discovering Association Rules
 Find patterns in group membership - Larson
 Findingg p
probabilityy of the co-occurrence of items in a collection
– Oracle book (http://download.oracle.com/docs/cd/B28359_01/datamine.111/b28129/market_basket.htm)
 Produce dependency rules which will predict occurrence of an
item or event based on occurrences of other items
 Discovering Sequential Patterns – Sequence Analysis
 Regression
 Detective Deviations from Normal
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Association
 Also called market basket analysis
 Customers who bought this book also bought this other book
in the same purchase at what rate?
 Patients who were treated with drug X developed side effect
B at a particular rate
rate, what else did the side effect B people
have in common?
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Association
Predictive or Descriptive?
Supervised or Unsupervised?
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Association
Predictive
Supervised
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Association – Predictive - Supervised
 Predict which second book a customer choosing a first book
might like
 Predict which patients undergoing treatment with drug X
will develop side effect B
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Data Mining - Task Types
 Classification
 Clustering
 Discovering Association Rules
 Discovering Sequential Patterns – Sequence Analysis
 Predict future routes based on past routes – Larson
 Given is a set of objects, with each object associated with its
own timeline of events, find rules that predict sequential
dependencies among different events – Swaroop and Golden
(http://www.rhsmith.umd.edu/faculty/bgolden/classes_links/2009_jan_data%20mining_BUDT%20758.pdf)
 Regression
 Detective Deviations from Normal
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Sequence Analysis
 A user is on web page A, what page is the user most likely to
navigate to next?
 A customer buys a Kindle. What is the customer most likely
to do next?
 Does the sequential pattern of events in an event log tell us
anything about server outages? Network outages?
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Sequence Analysis
Predictive or Descriptive?
Supervised or Unsupervised?
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Sequence Analysis
Predictive and Descriptive methods
Supervised and Unsupervised methods
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Sequence Analysis – Predictive and/or
Descriptive
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Data Mining - Task Types
 Classification
 Clustering
 Discovering Association Rules
 Discovering Sequential Patterns – Sequence Analysis
 Regression
 Predict the value of a continuous variable (as opposed to a
categorical variable) – Larson
 Regression predicts a number – Oracle book
(http://download.oracle.com/docs/cd/B28359_01/datamine.111/b28129/regress.htm)
 Detective Deviations from Normal
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Regression
 Here is data about loan applicants. Which are good risks and
which are poor risks?
 Which of our current customers are likely to increase their
current rate of spending if given an affinity card?
 Which consumers are likely to buy a new cell-phone
cell phone product
if I send them a direct mailing?
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Regression
Predictive or Descriptive?
Supervised or Unsupervised?
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Regression
Predictive
Supervised
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Regression – Predictive - Supervised
 Predict which loan applicants are good risks and which are
poor risks
 Predict which of our current customers are likely to increase
their current rate of spending if given an affinity card
 Predict which consumers are likely to buy a new cell-phone
cell phone
product if I send them a direct mailing
 Predict which patients will respond better to treatment A or
treatment B
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Data Mining - Task Types
 Classification
 Clustering
 Discovering Association Rules
 Discovering Sequential Patterns – Sequence Analysis
 Regression
 Detecting Deviations from Normal – Anomaly Detection
 Identify cases that are unusual within homogeneous data –
Oracle book (http://download.oracle.com/docs/cd/B28359_01/datamine.111/b28129/anomalies.htm)
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Detecting Deviations from Normal –
Anomaly Detection
 Is there anything unusual about this pattern of credit card
charges?
 Is there anything unusual about this event log that would
indicate an unauthorized intrusion?
 Is there any pattern here that indicates an unusual pattern of
accidents and treatments?
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Detecting Deviations from Normal
– Anomaly Detection
Predictive or Descriptive?
Supervised or Unsupervised?
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Detecting Deviations from Normal
– Anomaly Detection
Predictive
Supervised
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Anomaly Detection
– Predictive - Supervised
 This pattern of credit card charges implies a stolen credit
card
 This pattern of events in the event log indicates an
unauthorized intrusion
 This pattern of accidents and treatments indicates the
likelihood of insurance fraud
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Data Mining – Algorithms
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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Algorithms Available in Analysis
Services
 Decision Trees
 Linear Regression
 Naïve Bayes
 Clustering Algorithms
 Association Rules
 Sequence Clustering
 Time Series Analysis
 Neural Networks
 Logistic Regression
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Data Mining
By Susan Miertschin
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