Data Mining Tools Overview

1
Data Mining Tools Overview

• Business Intelligence for Managers

2
Data Mining DefinitionRevisited

• Analysis of large quantities of data
• Knowledge discovery in databases
• Extracting implicit, previously unknown
  information from large volumes of raw data

3
Instances and Features

• Typically, the database will be a collection of
  instances
• Each instance will have values for a given set of
  features
• From database theoryinstances are rows,
  features are columns

4
Classification

• Supervised learning
• Suppose instances have been categorized into
  classes and the database includes this
  categorization
• Goal using the knowledge in the database,
  classify a given instance

5
Classifiers
X1
X2
feature values
Y
X3
category
Classifier

Xn
DB
collection of instanceswith known categories
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Classifier intelligence

• A classifiers intelligence will be based on a
  dataset consisting of instances with known
  categories
• Typical goal of a classifier predict the
  category of a new instance that is rationally
  consistent with the dataset

7
BI Examples

• A loans officer in a bank uses a system that
  automatically approves or disapproves a loan
  application based on previous loan applications
  and decisions
• An admissions officer in a university uses a
  system that automatically makes an admission
  decision (accept, reject, wait-list), based on
  previous applicants data and decisions made on
  them

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Data mining method examplek - nearest neighbors

• For a given instance T, get the top k database
  instances that are nearest to T
• Select a reasonable distance measure
• Inspect the category of these k instances, choose
  the category C that represent the most instances
• Conclude that T belongs to category C

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Clustering(Chapter 5 of text)

• Unsupervised learning
• Classes/categories are not known, but unexpected
  groupings (clusters) are discovered
• Clustering provides insight into the population
  segments

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Clustering
Feature 2
Feature 1
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Goal of Clustering

• Input the database of instances, and possibly
  some predetermined number of clusters
• Output the same database of instances
  partitioned into clusters

12
BI Examples

• After clustering the current university student
  population, it was discovered that there is a
  large group of female marketing majors coming
  from a particular exclusive school who tend to
  get high grades
• business response focus recruitment on that
  school push the universitys marketing program
• Customer segment characteristics and spending
  patterns can direct business strategies

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Data mining method example k-means

• Guess the number of clusters (k)
• Guess cluster centers from the samples (these
  will be called centroids)
• Determine cluster membership based on the
  distance from the centroids
• Repeatedly refine the centroids by getting the
  average (mean) of the members of each cluster

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Summary

• Two sub-areas of data mining supervised
  (classification) and unsupervised (clustering)
  learning methods
• For both types of methods, intelligent systems
  can be created to support business decision making