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Renata Benda Prokeinova
Department of Statistics and Operation Research
FEM SUA in Nitra
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Data are any facts, numbers, or text that can
be processed by a computer.
Today, organizations are accumulating vast
and growing amounts of data in different
formats and different databases.
The patterns, associations, or relationships
among all this data can provide information.
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Information can be converted into knowledge
about historical patterns and future trends.
For example, summary information on retail
supermarket sales can be analyzed in light of
promotional efforts to provide knowledge of
consumer buying behavior.
Dramatic advances in data capture, processing power, data
transmission, and storage capabilities are enabling
organizations to integrate their various databases into
data warehouses.
Data warehousing is defined as a process of centralized data
management and retrieval.
Data warehousing, like data mining, is a relatively new term
although the concept itself has been around for years.
Data warehousing represents an ideal vision of maintaining
a central repository of all organizational data.
Centralization of data is needed to maximize user access
and analysis.
Technological advances are making this vision a reality for
many companies. And, equally advances in data analysis
software are allowing users to access this data freely. The
data analysis software is what supports data mining.
Basic view
 tons of data is collected, then quant wizards
work their arcane magic, and then they know
all of this amazing stuff
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tells us about very large and complex data
sets, the kinds of information that would be
readily apparent about small and simple
things.
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is a means of automating part this process to
detect interpretable patterns
Discovering information from data takes two
major forms: description and prediction
At the scale we are talking about, it is hard to
know what the data shows.
Data mining is used to simplify and summarize
the data in a manner that we can understand,
and then allow us to infer things about
specific cases based on the patterns we have
observed.
A company wants to launch an advertising campaign for a product.
Among its present customers the company wants to post product
information to those with a high probability of purchasing the
product.
The company has data describing the past customer behaviour and
personal data about each of its customers. There are also
customers who have already bought the product, e.g. in a trial
period.
The customers of the trial period are divided into two classes:
those who have bought the product and those who have not.
With this data a prediction model is created to predict the
probability of purchasing the product. After that the probability
of purchasing the product is predicted for all other customers.
Only those with a higher probability are addressed.
As a side effect the company learns with this data mining analysis
which are the relevant driver attributes of its customers buying a
specific product (or at least being very interested in it).
Analysis local buying patterns
story:
They discovered that when men bought diapers on
Thursdays and Saturdays, they also tended to buy beer.
Further analysis showed that these shoppers typically did
their weekly grocery shopping on Saturdays.
On Thursdays, however, they only bought a few items. The
retailer concluded that they purchased the beer to have it
available for the upcoming weekend.
The grocery chain could use this newly discovered
information in various ways to increase revenue.
For example, they could move the beer display closer to the
diaper display. And, they could make sure beer and
diapers were sold at full price on Thursdays.
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WalMart is pioneering massive data mining to
transform its supplier relationships. WalMart
captures point-of-sale transactions from over
2,900 stores in 6 countries and continuously
transmits this data to its massive 7.5 terabyte
Teradata data warehouse.
WalMart allows more than 3,500 suppliers, to
access data on their products and perform data
analyses. These suppliers use this data to identify
customer buying patterns at the store display
level. They use this information to manage local
store inventory and identify new merchandising
opportunities.
Data.Mining.Fox
can help in marketing to predict the purchase
probability of customers for a specific
product.
 Easy.Data.Mining
can add value by being profitably applied to
marketing challenges.
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Classes
Clusters
Associations
Sequential patterns
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Stored data is used to locate data in
predetermined groups. For example, a
restaurant chain could mine customer
purchase data to determine when customers
visit and what they typically order. This
information could be used to increase traffic
by having daily specials.
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Data items are grouped according to logical
relationships or consumer preferences. For
example, data can be mined to identify
market segments or consumer affinities.
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Data can be mined to identify associations.
The beer-diaper example is an example of
associative mining
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Data is mined to anticipate behaviour
patterns and trends.
For example, an outdoor equipment retailer
could predict the likelihood of a backpack
being purchased based on a consumer's
purchase of sleeping bags and hiking shoes.
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The most common and important form of
analysis is statistical analysis of data, if the
data are metric and quantitative in nature.
The number of respondents in a quantitative
Market Research projects can often be over a
thousand, making a large bunch of data
points within the data set.
After the data collected is cleaned for illogical responses and
missing responses, a next good step would be tabulating and
cross-tabulating respondents’ answers for all the questions.
The cross-tabulating based on various segments such as
demographic segments and other segments are useful in
validating the responses and making sense of the data.
Another useful statistic is the mean or the average. The average
response for all the respondents or a cluster within the sample is
good starting summary of the data.
For example, if the market research project is about understanding
the ability to pay for a certain product, then the average income
of the respondents could be the very first statistic that would
give a sense of the data. For instance, the average income of
1000 respondents is $1000. The average for various clusters can
then be calculated. Cluster averages such as average income of
male and female, average income of various age groups, average
income of various geographic locations, etc. would give a better
picture of the situation at hand.
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The mode is the value that has the maximum
number of occurrences.
The mode represents the highest peak of the
normal distribution curve. This means that
the normal distribution curve highest point
will correspond to the value of mode. The
mode is a good measure of location of data
when the variable is categorical.
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The middle value of ranked data is the
median. If the number of data points is even,
the median is calculated by taking the
average of the two middle values.
There are 50% of values larger than the
median in the data set and 50% lesser than
the median. Therefore, the median is the 50th
percentile. The median is a good measure of
central tendency for ordinal data.
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The range measures the spread of the data.
The spread is the distance between or the
gap between the largest and smallest value.
Thus, the range will be directly affected by
outliers. Therefore, it is advisable to remove
outliers by using box plot or any other tool
before any statistical analysis.
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The difference between the mean and an
observed value is called as a deviation from
the mean.
The variance is the average of the square of
the deviations from the mean for all the
values.
The variance is always a positive figure. If the
data points are clustered closely around the
mean, the variance is small. If the data points
are scattered dispersedly around the mean,
the variance is large
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The standard deviation is the square root of
variance.
The histogram is a summary graph showing a
count of the data points falling in various
ranges. The effect is a rough approximation
of the frequency distribution of the data.
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The groups of data are called classes, and in
the context of a histogram they are known as
bins, because one can think of them as
containers that accumulate data and "fill up"
at a rate equal to the frequency of that data
class.
The histogram of the frequency distribution
can be converted to a probability distribution
by dividing the tally in each group by the
total number of data points to give the
relative frequency.
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