Insurance Claims Settlement: find similar claims in the past
Real estate: Property price appraisal based on previous sales
K-Nearest Neighbor can be used for classification tasks.
Step 1: Using a chosen distance metric, compute the distance
between the n

The goal: ideal DM environment
All data mining algorithms want their input in tabular form rows &
columns as in a spreadsheet or database table
The columns
Contain data that describe aspects of the customer (e.g., sales
$ and quantity for each of product

General Algorithms
GAs can be used to try different combinations of parameters for building
models, to see which build parameters are best.
Assume we arrived at ten formulae, using a variety of neural nets run
on different sample data and with different r

often work well for classes that are hard to separate using linear
methods or the axis-parallel splits used by decision trees.
K-Nearest neighbor works well even when there is some missing data
K-Nearest neighbor is good at specifying which predictions h

We would maintain a population of solutions, with each solutions
having a score. Solutions with a higher fitness score are given a
higher probability of mating.
The Traveling Sales Person is a well-known route optimization
problem with applications to tr

Based on the concept of similarity
The distance metric can be, for instance, simple Euclidian distance or
weighted Euclidian distance. The Euclidian distance d(X,Y) between
two points, X = (x1, x2, ., xn) and Y = (y1, y2, ., yn) is:
Regular database quer

Principal Component Analysis -1
Open the dataset Open the file Utilities.xls, which gives data on 22
public utilities in the US.
Principal Component Analysis -2
In XLMiner, select Data Reduction and Exploration -> Principal
Components.
In the Principal Co

CF is a variant of MBR particularly well suited to personalized
recommendations
Starts with a history of peoples personal preferences
Uses a distance function people who like the same things are
close
Uses votes which are weighted by distances, so close

Survival Analysis aka Time-to-Event Analysis is very valuable for
understanding customers
Survival tells us when to start worrying about customers
Most important facet of customer behavior is the customers tenure
with us
The analysis of time to event dat

Based on an analogy to biological processes similar to neural
networks and memory-based reasoning techniques
Goal is to maximize the fitness
Are being utilized for
Complex scheduling
Resource optimization
Classification
Often used in tandem with other DM