Classical modeling techniques like statistics and clustering are difficult to apply to large datasets. Statistics involves collecting and describing data to find patterns and build predictive models. Clustering groups similar objects together based on distance between objects. These techniques were developed before terms like "data mining" and are still commonly used, but can struggle with very large datasets.

1. Q:Explain why it is not possible to analyze some large data sets using classical modeling
techniques. ?
Answer:
 If the goal is prediction accuracy, average many prediction models together.
 When testing many hypotheses, correct for multiple testing
 When you have data measured over space, distance, or time, you should smooth.
 Before you analyze your data with computers, be sure to plot it.
 Interactive analysis is the best way to really figure out what is going on in a data set.
 Know what your real sample size is.
 Unless you ran a randomized trial, potential confounders should keep you up at night’
 Define a metric for success up front.
 Make your code and data available and have smart people check it.
 Problem first not solution backward
Classical Techniques: Statistics, Neighborhoods and Clustering
The Classics
These two sections have been broken up based on when the data mining technique
was developed and when it became technically mature enough to be used for business,
especially for aiding in the optimization of customer relationship management
systems. Thus this section contains descriptions of techniques that have classically
been used for decades the next section represents techniques that have only been
widely used since the early 1980s.
The main techniques that we will discuss here are the ones that are used 99.9% of the
time on existing business problems. There are certainly many other ones as well as
proprietary techniques from particular vendors - but in general the industry is
converging to those techniques that work consistently and are understandable and
explainable.
Statistics:
By strict definition "statistics" or statistical techniques are not data mining. They were being
used long before the term data mining was coined to apply to business applications. However,
statistical techniques are driven by the data and are used to discover patterns and build predictive
models. And from the users perspective you will be faced with a conscious choice when solving
a data mining" problem as to whether you wish to attack it with statistical methods or other data
mining techniques. For this reason it is important to have some idea of how statistical techniques
work and how they can be applied.

2. “Statistics is a branch of mathematics concerning the collection and the description of
data. Usually statistics is considered to be one of those scary topics in college right up there with
chemistry and physics. However, statistics is probably a much friendlier branch of mathematics
because it really can be used every day. Statistics was in fact born from very humble beginnings
of real world problems from business, biology, and gambling.”
Clustering:
Unsupervised learning: Finds “natural” grouping of instances given un-labeled data.
“The process of grouping physical or abstract objects into classes of similar objects”.
What is a cluster
1. A cluster is a subset of objects which are “similar”
2. A subset of objects such that the distance between any two objects in the cluster is less than
the distance between any object in the cluster and any object not located inside it.
3. A connected region of a multidimensional space containing a relatively high density of
objects.
ClusterAnalysis:
Astronomy - aggregation of stars, galaxies, or super galaxies.
ProblemStatement :
Given a set of records (instances, examples, objects, observations, …), organize them into
clusters (groups, classes)

3. Example:
Nearest Neighbor Problem:
General Formulation:
 
point tonearestthepointeachFor
OutputDesired
P
dimensionsinpointsofseta:Input
21
Pp
ppp
dn
n

 
NearestNeighbor Problem:

4. Applications:
• Points could be web-page, closest neighbor is the most similar web-page
• Points could be people, closest neighbor could be the best friend
Points could be biological spices, the closest neighbor could be the closest spices.
2-Classify the following attributes as binary, discrete, or continuous. Also, classify
them as qualitative (nominal or ordinal) or quantitative (interval or ratio). Some
cases may have more than one interpretation, so briefly indicate your reasoning
(e.g., age in years; answer: discrete, quantitative, ratio).
(a) Time in terms of AM or PM.
(b) Brightness as measured by a light meter.
(c) Brightness as measured by people’s judgment.
(d) Angles as measured in degrees between 0 and 360.
(e) Bronze, Silver, and Gold medals at the Olympics.
(f) Height above sea level.
(g) Number of patients in a hospital.
(h) ISBN numbers for books.
(i) Ability to pass light in terms of the following values: opaque, translucent,
transparent.
(j) Military rank.
(k) Distance from the center of campus.
(l) Density of a substance in grams per cubic centimeter.
(m) Coats check number when you attend the event.
Answer:
(a) Brightness as measured by a light meter.
(b) Answer: Continuous, quantitative, ratio
(b) Angles as measured in degrees between 0 ◦ and 360 ◦ .
Answer: Continuous, quantitative, ratio

5. (c) Bronze, Silver, and Gold medals as awarded at the Olympics.
(d) Answer: Discrete, qualitative, ordinal
(d) Number of patients in a hospital.
Answer: Discrete, quantitative, ratio
(e) Ability to pass light in terms of the following values: opaque, translucent, transparent.
Answer: Discrete, qualitative, ordinal
(f) Military rank.
Answer: Discrete, qualitative, ordinal
(g) Density of a substance in grams per cubic centimeter.
Answer: Discrete, quantitative, ratio
(h) Coat check number. (When you attend an event, you can often give your coat to someone
who, in turn, gives you a number that you can use to claim your coat when you leave.)
Answer: Discrete, qualitative, nominal