#### Transcript The Data Mining Process

```Chapter 2
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Science Solution
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Fundamental concepts
 An important Principle of data science is that data
mining is a process with fairly well-understood stages.
 Some involve the application technology, such as the
automated discovery and evaluation of patterns
from data, while others mostly require an analyst’s
creativity, business knowledge, and common sense.
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Fundamental concepts
 Since the data mining process breaks up the overall
task of finding patterns from data into a set of welldefined subtasks, it is also useful for structuring
 This chapter introduces the data mining process, but
first we provide additional context by discussing
common types of data mining task.
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From Business Problems to Data Mining
 Each data-driven business decision-making problem
is unique, comprising its own combination of goals,
desires, constraints, and even personalities.
 The solutions to the subtasks can then be composed
to solve the overall problem. Some of subtasks are
unique to the particular business problem, but others
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From Business Problems to Data Mining
 Example: telecommunications churn problem(電信客

 Estimate from historical data the probability of a customer
terminating her contract shortly after it has expired.
 Despite the large number of specific data mining
algorithms developed over the years, there are only a
handful of fundamentally different types of tasks
 Individual(個體):entity. Ex: a customer or a business.
 Correlations(相關性):between a particular variable
describing an individual the company after their contracts
expired.
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Classification
 Classification(分類) and class probability estimation
attempt to predict, for each individual in a
population, which of a (small) set of classes this
individual belongs to.
 Example question: “Among all customers of
MegaTelCo, which are likely to respond to a given
offer?”
 Two classes: will respond and will not respond.
 Scoring or class probability estimation
 Score representing the Probability( quantification of
likelihood)
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Regression
 Regression(回歸)(“value estimation”) attempts to
estimate or predict, for each individual, the numerical
value of some variable for that individual.
 Example question: “ How much will a given customer
use the service?”
 Classification predicts whether something will happen.
 Regression predicts how much something will happen.
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Similarity matching
 Similarity matching(相似度配對) attempts to identify
similar individuals based on data known about them.
 Ex: IBM is interested in finding companies similar to
their best business customer, in order to focus their
sales force on the best opportunities.
 Recommendations
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Clustering
 Clustering(群集) attempts to group individuals in a
population together by their similarity, but not driven
by any specific purpose.
 Example question: “ Do our customers form natural
groups or segments?”
 Decision-making processes.
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Co-occurrence grouping
 Co-occurrence grouping(共生分群) attempts to find
association between entities based on transactions
involving them.
 Example question: “ What items are commonly
purchased together?”
 Ex: analyzing purchase records from a supermarket.
 Recommendation system
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Profiling
 Profiling(剖析)(also as behavior description(型為描述))
attempts to characterize the typical behavior o an
individual, group, or population.
 Example question: “ What is the typical cell phone
usage of this customer segment?”
 Profiling is often used establish behavioral norms for
anomaly detection applications.
 Fraud detection and monitoring for intrusions to computer
systems.
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 Link prediction(連結預測) attempts to predict
connections between data items, usually by
suggesting that a link should exist, and possibly also
estimating the srength of the link.
 “Since you and Karen share 10 friends, maybe you’d
like to be Karen’s friend?”
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Data reduction
 Data reduction(資料縮減) attempts to take a large set
of data and replace it with a smaller set of data that
contains much of the important information in the
larger set.
 Ex: a massive dataset on consumer movie-viewing
preferences may be reduce to a much smaller
dataset revealing the customer taste preferences.
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Causal modeling
 Causal modeling attempts to help us understand
what events or actions actually influence others.
 Ex: consider that we use predictive modeling to
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Supervised vs. Unsupervised Methods
 Unsupervised: no specific purpose or target.
 “Do our customers nataturally fall into different group?”
 Supervised: specific target defined.
 “ Can we find groups of customers who have particularly
high likelihoods of canceling their service soon after their
contracts expires?”
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Supervised vs. Unsupervised Methods
 Supervised data mining: there must be data on the
target.
 Tow subclasses of supervised data mining:
 Classification
 “ Will this customer purchase service S1 if given incentive I?”
 “Which service package( S1, S2 , or none) will a customer likely
purchase if given incentive I?”
 Regression
 “How much will this customer use the service”
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Supervised vs. Unsupervised Methods
 A vital part in the early stages of the data mining
process
 To decide whether the line of attack will be supervised or
unsupervised.
 If supervised, to produce a precise definition of a target
variable. This variable must be specific quantity that will be
the focus of the data mining.
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Data Mining and Its Result
 Distinction pertaining to mining data:
 Mining the data to find patterns and build models.
 Using the results of data mining.
 Churn example.
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Data Mining and Its Result
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The Data Mining Process
 Cross Industry
Standard
Process for
Data Mining
process.
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 It is vital to understand the problem to solved.
 A part of the craft where the analysts’ creativity plays
a large role.
 The design team should think carefully about the use
scenario.
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Data Understanding
 The data comprise the available raw material from
which the solution will be built.
 Estimating the costs and benefits of each data
source and deciding whether further investment is
merited.
 Ex:
 Credit card fraud
 Medicare fraud
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Data Preparation
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Data preparation
 Often proceeds along with data understanding.
 Ex.
1. converting data to tabular format.
2. removing or inferring missing values.
3. converting data to different types.
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Data preparation
 Leaks
a variable collected in historical data gives information on
the target variable-information that appears in historical
data but is not actually available when the decision has to
Leakage must be considered carefully during data preparation.
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Modeling
 Output of modeling is some sort of model or pattern
capturing regularities in the data.
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Evaluation
 Assess the data mining results rigorously and to gain
confidence that they are valid and reliable before
moving on.
 Includes both quantitative and qualitative
assessments.
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Deployment
 Put into real use in order to realize some return on
investment.
 The clearest cases of deployment involve
implementing a predictive model in some information
 ex. Churn example
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Deployment
 The data mining techniques themselves are
deployment.
 Two reasons
1. the world may change faster than the data science team
can adapt, as with fraud and intrusion detection.
science team to manually curated each model individually.
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Deployment
 Can also be mush less “technical”
 It is not necessary to fail in deployment to start the
cycle again. The Evaluation stage may reveal that
results are not good enough to deploy.
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Implications for Managing the Data
Science Team
 It is tempting - but usually a mistake - to view the data
mining process as a software development cycle.
 Software skills versus analytics skills
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Other Analytics Techniques and
Technologies
 Present six groups of related analytic techniques.
 Comparisons and contrasts with data mining.
 Data mining => automated search for knowledge,
patterns, or regularities from data.
 Business analyst => to recognize what sort of analytic
technique is appropriate for addressing a particular
problem.
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Statistics
 Two different uses in business analytics.
1.
used as a catchall term for the computation of particular
numeric values of interest from data.
2.
denote the field of study that goes by that name.
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Data Querying
 A specific request for a subset of data or for statistics
about data, formulated in a technical language and
posed to a database system.
 Differs fundamentally from data mining in that there is
no discovery of patterns or models.
 Ex: select * from customers where age >45
= ‘m’ and domicile = ‘ne’
and sex
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Data Querying
 On-line Analytical Processing (OLAP)
easy-to-use GUI to query large data collections
 Data mining tools generally can incorporate new
dimensions of analysis easily as part of the exploration.
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Data Warehousing
 Collect and coalesce data from across an enterprise,
often from multiple transaction-processing systems,
each with its own database.
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Regression Analysis
 This will involve estimating or predicting values for
cases that are not in the analyze data set.
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Machine Learning and Data Mining
 A field of study arose as a subfield of Artificial
Intelligence, which was concerned with methods for
improving the knowledge or performance of an
intelligent agent over time.
 KDD focused on concerns raised by examining realworld applications.
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Techniques
1.
who are the most profitable customers?
2.
Is there really a difference between the profitable
customers and the average customer?
3.
But who really are these customers? Can I
characterize them?
4.
Will some particular new customer be profitable?How
much revenue should I expect this customer to
generate?
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summary
 Data mining is a craft. As with many crafts, there is a
well-defined process that can help to increase the
likelihood of a successful result.
 We will refer back to the data mining process
repeatedly throughout the book, showing how each
fundamental concept fits in.
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THE END
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