Data mining is the process of discovering patterns in large data sets. The overall goal is to extract useful information that can be understood and used. Key tasks include classification, regression, clustering, summarization, and dependency modeling. Common data mining methods are statistical analysis, decision trees, association rules, and neural networks. Data mining has various applications like direct marketing, market segmentation, customer churn prediction, and market basket analysis. It allows for more effective decision making, prediction, and privacy concerns need to be addressed.
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Datamining
1. WHAT IS DATA MINING
Data Mining (The analysis step of Knowledge Discovery
in Databases” Process or KDD), an interdisciplinary
subfield of computer Science, is the computational
process of discovering patterns in large data sets
involving methods at the intersection of artificial
intelligence, machine learning, statistics, and database
management systems.
2. BASIC-DEFINITIONS OF DATA
MINING
• The discovery of new, non-obvious, valuable information from
a large collection of raw data
• Data Mining (DM) is the core of the KDD [Knowledge Discovery in
Databases] process, involving the inferring of algorithms that explore
the data, develop the model and discover previously unknown
patterns.
• The set of activities used to find new, hidden or unexpected
patterns in data
3. DEFINITIONS OF DATA MINING
The detection of patterns from existing data.
pattern
1. A consistent, trait, feature, or method.
2. Any combination of values that contain meaning within the
context or domain for which they are being reviewed
4. DATA MINING -CONTINUED
The overall goal of the data mining process is to extract
information from a data set and transform it into an
understandable structure for further use:
(Predictive analytics)
Discovering meaningful new corrections, patterns,
trends.
Example : Forecasting
5. DATA ANALYTICS/PREDICTIVE
ANALYTICS
Data analytics (DA) is the science of
examining raw data with the purpose of
drawing conclusions about that information.
Data analytics is used in many industries to
allow companies and organization to make
better business decisions and in the sciences
to verify or disprove existing models or
theories
6. Data analytics is distinguished from Data mining by the scope,
purpose and focus of the analysis. Data miners sort through
huge data sets using sophisticated software to identify
undiscovered patterns and establish hidden relationships. Data
analytics focuses on inference, the process of deriving a
based solely on what is already known by the researcher.
7. PREDICTIVE ANALYTICS - FOCUS
Uses lower level of Granularity, meaning it looks at the
individual level. Instead of looking at which candidate
will win the Presidential election in the state of Ohio,
which is forecasting. It looks at the individual level.
Which person is voting for or against.
Predicts which individuals can be persuaded, which ones
will not change, etc. Now with this information we ca
change the outcome of the race.
Obama used this technique very well.
8. EMERGING TECHNOLOGY
Data mining is one of the “10 emerging
technologies that will change the world” listed by
the MIT Technology Review (Larose).
There is no doubt why many firms embrace data
mining in their operations. An article in
Information System Management points out that
“data mining has become a widely accepted
process for organizations to enhance their
organizational performance and gain a
competitive advantage”
9. DATA MINING: BUSINESS
• What is it?
Decision making
Marketing
Detecting Fraud
This technology is popular with many businesses because it allows them to
learn more about their customers, prevent frauds and identity theft, and also
make smart marketing decisions
10. Keys to a Successful Data Mining Project
• Credible source of data
• Knowledgeable personnel
• Appropriate algorithms
11. Classification classify a data item into one of
several predefined classes
Regression map a data item to a real-value
prediction variable
Clustering identify a finite set of
categories or clusters to
describe the data
Summarization find a compact description for a
set (or subset) of data
Dependency Modeling describe significant dependencies
between variables or between the
values of a feature
Change and Deviation
Detection
Discover the most significant
changes
PRIMARY TASKS OF DATA MINING
12. SOME OF THE COMMONLY USED DATA
MINING METHODS ARE:
• Statistical Data Analysis
• Cluster Analysis
• Decision Trees and Decision Rules
• Association Rules
• Artificial Neural Networks
• Genetic Algorithms
• Fuzzy Sets and Fuzzy Logic
13. DATA MINING APPLICATIONS
In direct marketing a company saves much time by marketing to
prospects that would have the highest reply rate. Instead of
random selection on which customers to pick for their surveys, a
company could use direct marketing from data mining to find
the “correct” customers to ask.
14. DIRECT MARKETING USING DATA MINING, GIVES
US 3% CONVERSION
Identifies smaller group, example ¼ of population and
gets a higher conversion, 3% ,
15. DATA MINING APPLICATIONS
Market segmentation is used in data mining in order to identify
the common characteristics of customers who buy the products
from one’s company.
With market segmentation, you will be able to find behaviors that
are common among your customers. As a company seeks
customer’s trends, it helps them find necessities in order to help
them improve their business.
16. DATA MINING APPLICATIONS
Customer churn predicts which customers will have a
change of heart
towards your company and join another company
(competitor). Although customer churns are negative
to one’s business, it allows the corporation to seek out
the problem they are facing and create solutions.
18. DATA MINING APPLICATIONS
Market basket analysis- involves researching
customer characteristics in respect to their purchase
patterns
Example: Ralphs Club Card
Cereal and Milk
20. PREDICTION BASED ON DATA
MINING/PREDICTIVE ANALYSIS
Examples of real life.
Target – can predict which customers will be pregnant
Hospitals can predict which payments may need to be
admitted
Credit card – can predict which customers may miss
their payment based upon where card is used.
Example Bar-alcohol=missed payments
22. DATA MINING APPLICATIONS
Class identification, which consists of mathematical taxonomy and
concept clustering. Mathematical taxonomy focuses on what makes
the members of a certain class similar, as opposed to differentiating
one class from another.
For example, Ralphs can classify its customers based on their income
or past purchases
23. DATA MINING APPLICATIONS
Concept clustering - determines clusters according to attribute similarity.
Consider the pattern a purchase of toys for age group 3–5 years, is
followed by purchase of kid’s bicycle within 6 months about 90% of the
time by high income customers, which was discovered by data mining.
The Company can identify the prospective customers for kid’s bicycle
based
on toy purchase details and adjust the mail catalog accordingly.
24. DATA MINING APPLICATIONS
Deviation analysis, A deviation can be fraud or a change. In the
past, such deviations were difficult to detect in time to take
corrective action. Data mining tools help identify such deviations
.
For example, a higher than normal credit purchase on a credit card
can be a fraud, or a genuine purchase by the customer. Once a
deviation has been discovered as a fraud, the company takes
steps to prevent such frauds and initiates corrective action
26. •Sensitive information
Data mining increase
incentives to get
more sensitive data
Seeing into private
future- Target
Do we have the right
Employers try to
predict churn
PRIVACY
27. • Types
o –Coverage or frame error
o –Sampling error
o –Nonresponsive error
o –Measurement error
• Flawed data
RESPONSE BIAS ISSUES
28. DATA MINING IN MEDICAL
The most recent and most promising use of data mining has been the
development of data mining
tools for the medical sector. The use of
data mining to extract patterns from medical data provides near
endless opportunities for symptom trend detection, earlier
detection of illness, DNA trend analysis and improved patient
reactions to medicines. These many advantages allow doctors and
hospitals to be more effective and more efficient.
29. ADVANTAGES OF DATA MINING: MEDICINE
• Earlier detection of illness
• Symptom trends
• Data analysis
• Improved drug reactions
30. • No uniform language - Medical
• Incomplete records
• Privacy
DISADVANTAGES OF DATA MINING:
MEDICINE
31. DATA MINING - MEDICAL
How data mining is actually used to analyze individual data can become
quite complex due to the data. The goal of the process is to take the
medical data which contain many attributes and determine which ones
are actually relevant to the diagnosis, symptom or result.
Two methods used in medical data mining are clustering, discussed
previously and biclustering.
Notas do Editor
Data mining is the process of detecting patterns in existing database. For a successful data
mining project, credible source, knowledgeable personnel, and appropriate methodology are the
key factors. Today data mining is widely implemented in many fields. In business, data mining
enables firms to better understand their customers, prevent theft and fraud, and make better
business decision. However, there are issues regarding privacy, security, and misuse of
information as well. In addition, marketers find data mining helpful in strategies development,
class identification, and deviation analysis even though privacy issue and response bias are still
the problems. Furthermore, data mining provides new opportunities for symptom trend
detection, early detection of new illnesses, DNA analysis, and the study of patients’ reactions to
medicines. Using data mining in medical industry, on the other hand, has a number of
disadvantages including lack of uniform code, outdated translation, huge amount of information,
and privacy issues.
![BASIC-DEFINITIONS OF DATA
MINING
• The discovery of new, non-obvious, valuable information from
a large collection of raw data
• Data Mining (DM) is the core of the KDD [Knowledge Discovery in
Databases] process, involving the inferring of algorithms that explore
the data, develop the model and discover previously unknown
patterns.
• The set of activities used to find new, hidden or unexpected
patterns in data](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)