The document provides an overview of data mining techniques and processes. It discusses data mining as the process of extracting knowledge from large amounts of data. It describes common data mining tasks like classification, regression, clustering, and association rule learning. It also outlines popular data mining processes like CRISP-DM and SEMMA that involve steps of business understanding, data preparation, modeling, evaluation and deployment. Decision trees are presented as a popular classification technique that uses a tree structure to split data into nodes and leaves to classify examples.

2. OUTLINE
 INTRODUCTİON TO DATA MINING
 KNOWLEDGE DISCOVERY PROCESS
 DATA MİNİNG TECHNİCS AND TASKS
 APPLICATION EXAMPLES
 CONCLUSİON

3. OUTLINE
 INTRODUCTİON TO DATA MINING
 KNOWLEDGE DISCOVERY PROCESS
 DATA MİNİNG TECHNİCS AND TASKS
 APPLICATION EXAMPLES
 CONCLUSİON

4. Origine of data mining
 The term "Data Mining" appeared around 1990 in the database community .
 It Draws ideas from maching learning/ Artificial Intelligence, statistics, data systems and
other disciplines.
Data
mining
Artificial
Intelligence
Machine
learning
statistics
Data
systems
Other
disciplines

5.  The Explosive Growth of Data:
 More data is generated:
 Web, text, images …
 Business transactions, calls, ...
 Scientific data: astronomy, biology, etc
 More data is captured:
 Storage technology faster and cheaper
 The abundance of data, coupled with the need for powerful data analysis tools, has been
described as a ‘’ data rich but information poor situation’’
Why Data mining?
What will I
do with
these data?

6. What is Data Mining?
Data Mining is defined as the procedure of extracting information
from huge sets of data. In other words, we can say that data mining is
mining knowledge from data.

7. Why is Data Mining important?
 Changes in the Business Environment
 Customers becoming more demanding
 Markets are saturated.
 Drivers
 Focus on the customer, competition, and data assets

8. Why is Data Mining important (cont)
 Credit ratings/targeted marketing:
 Given a database of 100,000 names, which persons are the least
likely to default on their credit cards?
 Identify likely responders to sales promotions
 Fraud detection
 Which types of transactions are likely to be fraudulent, given the
demographics and transactional history of a particular
customer?
Data Mining helps extract such information

9. Database Processing vs. Data Mining
Processing
 Query
 Well defined
 SQL
 Query
 Poorly defined
 No precise query language
 Output
– Precise
–Subset of database
 Output
– Fuzzy
–Not a subset of database

10. Query Examples
 Database
 Data Mining
– Find all customers who have purchased milk
– Find all items which are frequently purchased with milk. (association rules)
– Find all credit applicants with last name of Michael.
– Identify customers who have purchased more than $10,000 in the last month.
– Find all credit applicants who are poor credit risks. (classification)
– Identify customers with similar buying habits. (Clustering)

11.  A data warehouse is a repository of information collected from multiple
sources, stored under a unified schema, and that usually resides at a
site.
 Machine learning: the study of computer algorithms to learn in other to
improve automatically through experience.
 Data base: the science and technology of collecting, storing and
managing data so users can retrieve, add, update or remove such data.
 Data mining: is mining knowledge from data. it is sometimes referred to
Knowledge Discovery in Databases (KDD)
Data mining vs other disciplines

12. OUTLINE
 INTRODUCTİON TO DATA MINING
 KNOWLEDGE DISCOVERY PROCESS
 DATA MİNİNG TECHNİCS AND TASKS
 APPLICATION EXAMPLES
 CONCLUSİON

13. Data Mining vs. KDD
 Knowledge Discovery in Databases (KDD): is the process of finding useful
information and patterns in data.
 Data mining is one of the tasks in the process of knowledge discovery from the
database. It is the Use of algorithms to extract the information and patterns derived by
the KDD process.

14. KDD PROCESS
Preprocessed data
Transformed
data
Patterns
Knowledge
selection
Data
mining
Raw
data
Target
data

15.  Selection: Obtain data from various sources.
 Pre processing: Cleanse data
 Transformation: Convert to common format. Transform to new format.
 Data mining: apply algorithms to the data to find the patterns and evaluate patterns of
discovered knowledge.
 Interpretation: Present results to user in meaningful manner.
KDD PROCESS ( continued)

16. CRISP-DM PROCESS MODEL
 It is a data mining process model that describes commonly used approaches that data mining
experts use to tackle problems.
CRISP-DM breaks the process of data mining into six major phases:
 BUSINESS UNDERSTANDING: This is the first phase of CRISP-DM process which focuses on and uncovers
important factors including success criteria, business and data mining objectives and requirements as well as
business terminologies and technical terms.
 DATA UNDERSTANDING: This is the second phase of CRISP-DM process which focuses on data collection,
checking quality and exploring of data to get insight of data to form hypotheses for hidden information.
 DATA PREPARATION: This phase focuses on selection and preparation of final data set. This phase may include
many tasks records, table and attributes selection as well as cleaning and transformation of data.
 MODELING: This is the fourth phase of CRISP-DM process selection and application of various modeling
techniques. Different parameters are set and different models are built for same data mining problem.
 EVALUATION: The process which focuses on evaluation of obtained models and deciding of how to use the
results. Interpretation of the model depends upon the algorithm and models can be evaluated to review whether
achieves the objectives properly or not.
 DEPLOYMENT:This phase focuses on determining the use of obtain knowledge and results.ıt also focuses on
organizing, reporting and presenting the gained knowledge when needed.
(CRoss-Industry Standard Process for Data Mining)

17. Illustration

18. SEMMA PROCESS MODEL
It offers and allows understanding, organization, development and maintenance of data mining
projects. It helps in providing the solutions for business problems and goals.
 Sample: This stage consists on sampling the data by extracting a portion of a large data set
big enough to contain the significant information, yet small enough to manipulate quickly.
This stage is pointed out as being optional.
 Explore: This stage consists on the exploration of the data by searching for unanticipated
trends and anomalies in order to gain understanding and ideas.
 Modify: This stage consists on the modification of the data by creating, selecting, and
transforming the variables to focus the model selection process.
 Model: This stage consists on modeling the data by allowing the software to search
automatically for a combination of data that reliably predicts a desired outcome.
 Assess: This stage consists on assessing the data by evaluating the usefulness and reliability of
the findings from the data mining process and estimate how well it performs.
(Sample, Explore, Modify, Model, and Access)

19. Illustration

20. OUTLINE
 INTRODUCTİON TO DATA MINING
 KNOWLEDGE DISCOVERY PROCESS
 DATA MİNİNG TECHNİCS AND TASKS
 APPLICATION EXAMPLES
 CONCLUSİON

21. Data Mining
DescriptivePredictive
classification regression Time Series
Analysis
prediction clustering summarization Association rules Sequence
Discovery
Data Mining Task
Predictive model are used to
predict about unknown values
from known values.
Descriptive model are used
to find human-
interpretable patterns that
describe the data

22. Common Data Mining Tasks
Data mining involves six common classes of tasks Data mining involves six common classes of
tasks.
Classification: is the task of generalizing known structure to apply to new data.
Regression: is used to map a data item to a real valued prediction variable.
Clustering: is the task of discovering groups and structures in the data that are in some way
or another "similar", without using known structures in the data
Association Rules learning : Searches for relationships between variables
Anomaly detection: The identification of unusual data records, that might be interesting or
data errors that require further investigation
Summarization: providing a more compact representation of the data set, including
visualization and report generation.

23. 1. Classification

24. Classification
Classification is the process of finding a model that describes the data classes or concepts.
Goal of classification is to build structures from examples of past decisions that can be used to make
decisions for unseen cases.
For example, we want to classify an e-
mail as "legitimate" or "spam"

25. CLASSIFICATION: THE PROCESS
 In classification, we are given a set of labeled examples
 These examples are records/instances in the format (x, y) where x is a
vector and y is the class attribute, commonly a scalar
 The classification task is to build model that maps x to y
 Our task is to find a mapping f such that f(x) = y

26. CLASSIFICATION: THE PROCESS

27. CLASSIFICATION: AN EMAIL EXAMPLE
 A set of emails is given where users have
manually identified spam versus non-spam
 Our task is to use a set of features such as words
in the email (x) to identify spam/non-spam
status of the email (y)
 In this case, classes are
y = {spam, non-spam}
 What would it be dealt with in a social setting?

28. Classification methods
 Decision tree learning
 Naive Bayes learning
 K-nearest neighbor classifier
 ….

29. Decision tree
Decision tree is a classifier in the form of a tree structure. It consists of nodes, branches and
leaves.
 The top node is called the root node.
The tree always starts from the root node and grows down by splitting the data at each level
into new nodes. The root node contains the entire data set (all data records), and child nodes
hold respective subsets of that set.
 All nodes are connected by branches.
 Nodes that are at the end of branches are called terminal nodes, or leaves.

30. Illustration
Root Node
Leaf Node Leaf Node
Branches
Set of possible answers Set of possible answers

31. Some Induction of Decision Trees Systems
 ID3 ( Iterative Dichotomiser 3 )
 C4.5 : improved version of ID3; it uses gain ratio as splitting criteria .
 CART (Classification and Regression Trees ): is a nonparametric decision tree algorithm.It
produces either classification or regression trees, based on whether the response variable is
categorical or continuous.
 CHAID (chi-squared automatic interaction detector) : focus on dividing a data set into exclusive
exclusive and exhaustive segments that differ with respect to the response variable.
 SPRINT (Scalable Parallelizable Induction of Decision Tree algorithm) :is also fast and highly
scalable, and there is no storage constraint on larger data sets in SPRINT
 ……
There are many specific decision tree algorithms.

32. ID3, A DECISION TREE ALGORITHM
Use information gain (entropy) to determine how well an attribute separates the training data
according to the class attribute value
 p+ is the proportion of positive examples in D
 p- is the proportion of negative examples in D
In a dataset containing ten examples, 7 have a positive class attribute value and 3
have a negative class attribute value [7+, 3-]:
If the numbers of positive and negative examples in the set are equal, then the entropy is 1

33. DECISION TREE: EXAMPLE 1
outlook temperature humidity windy play
sunny 85 85 FALSE no
sunny 80 90 TRUE no
overcast 83 86 FALSE yes
rainy 70 96 FALSE yes
rainy 68 80 FALSE yes
rainy 65 70 TRUE no
overcast 64 65 TRUE yes
sunny 72 95 FALSE no
sunny 69 70 FALSE yes
rainy 75 80 FALSE yes
sunny 75 70 TRUE yes
overcast 72 90 TRUE yes
overcast 81 75 FALSE yes
rainy 71 91 TRUE no

34. DECISION TREE: EXAMPLE 2
Learned Decision Tree 1 Learned Decision Tree 2
Class Labels

35. Decision Tree Advantages and Limitations
 Advantages:
 Easy to understand.
 Easy to generate rules
 limitations:
 May suffer from over fitting.
 Handling of missing and inconsistent data, they can produce reliable outcomes only when
they deal with “clean” data.
 ….
Despite all the limitations, decision trees have become the most successful technology
used for data mining

36. BAYESIAN CLASSIFICATION
 Probabilistic prediction: Predict multiple hypotheses, weighted by their
probabilities.
 Standard: Even when Bayesian methods are computationally intractable,
they can provide a standard of optimal decision making against which
other methods can be measured

37. NAIVE BAYES CLASSIFIER
The probabilistic model of NBC is to find the probability of a certain class given multiple
disjoint (assumed) events.
class variable the instance features
Then class attribute value for instance X
Assuming that variables are independent

38. ILLUSTRATION

39. K-Nearest Neighbor
KNN is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure.
k-nearest neighbor employs the neighbors of a data point to perform classification
The instance being classified is assigned the label that the majority of k neighbors’ labels
When k = 1, the closest neighbor’s label is used as the predicted label for the instance being classified
For determining the neighbors, distance is computed based on some distance metric, e.g., Euclidean distance

40. KNN Illustration
$0
$50,000
$100,000
$150,000
$200,000
$250,000
0 20 40 60 80
Non-Default
Default
Age
Loan$

41. KNN Classification – Distance
Age Loan Default Distance
25 $40,000 N 102000
35 $60,000 N 82000
45 $80,000 N 62000
20 $20,000 N 122000
35 $120,000 N 22000
52 $18,000 N 124000
23 $95,000 Y 47000
40 $62,000 Y 80000
60 $100,000 Y 42000
48 $220,000 Y 78000
33 $150,000 Y 8000
48 $142,000 ?
2
21
2
21 )()( yyxxD Euclidean Distance

42. KNN Classification – Standardized
Distance
Age Loan Default Distance
0.125 0.11 N 0.7652
0.375 0.21 N 0.5200
0.625 0.31 N 0.3160
0 0.01 N 0.9245
0.375 0.50 N 0.3428
0.8 0.00 N 0.6220
0.075 0.38 Y 0.6669
0.5 0.22 Y 0.4437
1 0.41 Y 0.3650
0.7 1.00 Y 0.3861
0.325 0.65 Y 0.3771
0.7 0.61 ?
MinMax
MinX
Xs


Standardized Variable

43. KNN Regression - Distance
Age Loan House Price Index Distance
25 $40,000 135 102000
35 $60,000 256 82000
45 $80,000 231 62000
20 $20,000 267 122000
35 $120,000 139 22000
52 $18,000 150 124000
23 $95,000 127 47000
40 $62,000 216 80000
60 $100,000 139 42000
48 $220,000 250 78000
33 $150,000 264 8000
48 $142,000 ?
2
21
2
21 )()( yyxxD 

44. KNN Regression – Standardized Distance
Age Loan House Price Index Distance
0.125 0.11 135 0.7652
0.375 0.21 256 0.5200
0.625 0.31 231 0.3160
0 0.01 267 0.9245
0.375 0.50 139 0.3428
0.8 0.00 150 0.6220
0.075 0.38 127 0.6669
0.5 0.22 216 0.4437
1 0.41 139 0.3650
0.7 1.00 250 0.3861
0.325 0.65 264 0.3771
0.7 0.61 ?
MinMax
MinX
Xs




45. KNN – Number of Neighbors
If K=1, select the nearest neighbor
If K>1,
For classification select the most frequent neighbor.
For regression calculate the average of K neighbors.

46. KNN - Applications
 Classification and Interpretation
legal, medical, news, banking
 Problem-solving
planning, pronunciation
 Function learning
dynamic control

47. KNN Advantages/limitation
 Advantages :
 Can be applied to the data from any distribution
for example, data does not have to be separable with a linear boundary
 Very simple and intuitive
 …..
 limitation
 Choosing k may be tricky
 Need large number of samples for accuracy

48. Classification: other approaches
 Support Vector Machines algorithm is supervised learning model with
associated learning algorithms that analyze data and recognize patterns,
which is based on statistical learning theory.
 Rules: The extraction of useful if-then rules from data based on statistical
significance
 Genetic Algorithms: Optimization techniques that use processes such as
genetic combination, mutation, and natural selection in a design based on
the concepts of natural evolution.
 …
48

49. 2. Regression
 Predict future values based on past values.
 Linear Regression assumes linear relationship exists.
y = c0 + c1 x1 + … + cn xn
 Find values to best fit the data.

50. REGRESSION
Regression analysis includes techniques of modeling and analyzing the relationship between a
dependent variable and one or more independent variables
 Regression analysis is widely used for prediction and forecasting
 It can be used to infer relationships between the independent and dependent variables.

51. LINEAR REGRESSION: EXAMPLE
Years of
experience
Salary ($K)
3 30
8 57
9 64
13 72
3 36
6 43
11 59
21 90
1 20
16 83

52. Clustering

53. CLUSTERING
 Clustering is a form of unsupervised learning
 Clustering algorithms group together similar items
It is a technique for grouping together items that are similar in some way –
according to some criteria.

54. Illustration
x
Cluster 1
Cluster 2
Cluster 3
y

55. 55
Example
 Example input database: Two numerical
variables.
Age Salary
20 40
25 50
24 45
23 50
40 80
45 85
42 87
35 82
70 30
Customer Demographics
0
10
20
30
40
50
60
70
80
90
100
0 20 40 60 80
Age
Salaryin$10K
Customers

56. Clustering algorithms
 Distance-based algorithms
• K-Means
 Hierarchical algorithms
 …

57. K-MEANS
 It partitions the given data into k clusters and each cluster has a cluster center, called
centroid.

58. HIERARCHICAL CLUSTERING
Hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of
clusters.
 Strategies for hierarchical clustering generally fall into two types:
 Agglomerative: This is a "bottom up" approach: each observation starts in its own cluster, and
pairs of clusters are merged as one moves up the hierarchy.
 Divisive: This is a "top down" approach: all observations start in one cluster, and splits are
performed recursively as one moves down the hierarchy.

59. ILLUSTRATION

60. Clustering Application
 Market Segmentation:
 Goal: subdivide a market into distinct subsets of customers where any
subset may conceivably be selected as a market target to be reached
with a distinct marketing mix.
 Document Clustering:
 Goal: To find groups of documents that are similar to each other based on the
important terms appearing in them.
 Biology: categorize genes with similar functionalities.
 ……

61. Association Rules
TID Produce
1 MILK, BREAD, EGGS
2 BREAD, SUGAR
3 BREAD, CEREAL
4 MILK, BREAD, SUGAR
5 MILK, CEREAL
6 BREAD, CEREAL
7 MILK, CEREAL
8 MILK, BREAD, CEREAL, EGGS
9 MILK, BREAD, CEREAL
61
Transactions
Frequent Itemsets:
Milk, Bread (4)
Bread, Cereal (3)
Milk, Bread, Cereal (2)
…
Rules:
Milk => Bread (66%)
Techniques that employ association search all details from operational
systems for patterns with a high probability of repetition
Example: Market Basket Analysis

62. Association Rule Discovery: Application
 Supermarket shelf management: To identify items
that are bought together by sufficiently many customers.

63. Visualization
 Visualizing the data to facilitate human discovery
 Presenting the discovered results in a visually "nice"
way.
63
İllustration

64. Visualization techniques
 Geometric techniques: scatterplots matrices, Hyperslice, parallel
coordinates
 Pixel-oriented techniques: simple line-by-line, spiral and circle segments
 Hierarchical techniques: Treemap, cone trees
 Graph-based techniques: 2D and 3D graph
 Distortion techniques: hyperbolic tree, fisheye view, perspective wall
 User interaction: brushing, linking, dynamic projections and rotations,
dynamic queries

65. Advantages of visualization
techniques
 • Visual data exploration can easily deal with very large, highly non
homogeneous and noisy amount of data
 • Visual data exploration requires no understanding of complex
mathematical or statistical algorithms
 • Visualization techniques provide a qualitative overview useful for further
quantitative analysis

66. Summarization 66
Abstraction or generalization of data resulting in a smaller set which gives
general overview of a data.

67. Data mining software tools
Orange
Weka
Rattle GUI
RapidMiner
…..

68. OUTLINE
 INTRODUCTİON TO DATA MINING
 KNOWLEDGE DISCOVERY PROCESS
 DATA MİNİNG TECHNİCS AND TASKS
 APPLICATION EXAMPLES
 CONCLUSİON

69. Applications
 Banking: loan/credit card approval
 predict good customers based on old customers
 Customer relationship management:
 identify those who are likely to leave for a competitor.
 Targeted marketing:
 identify likely responders to promotions
 Fraud detection: telecommunications, financial transactions
 from an online stream of event identify fraudulent events
 Manufacturing and production:
 automatically adjust knobs when process parameter changes

70. Applications (continued)
 Medicine: disease outcome, effectiveness of treatments
 analyze patient disease history: find relationship between diseases
 Molecular/Pharmaceutical: identify new drugs
 Scientific data analysis:
 identify new galaxies by searching for sub clusters
 Web site/store design and promotion:
 find affinity of visitor to pages and modify layout

71. OUTLINE
 INTRODUCTİON TO DATA MINING
 KNOWLEDGE DISCOVERY PROCESS
 DATA MİNİNG TECHNİCS AND TASKS
 APPLICATION EXAMPLES
 CONCLUSİON

72. Conclusion
 Data mining: discovering interesting knowledge from large amounts of
data .
 A KDD process includes data selection , transformation, data mining,
pattern evaluation, and knowledge presentation

73. THANKS
END
QUESTIONS?

74. Next time
 Some Data mining applications in details
 Data warehousing process and data mining
 Other details…

75. Additional Resources
1. http://www.twocrows.com/glossary.htm, “Two Crows, Data Mining
Glossary”
2. http://en.wikipedia.org/wiki/Data_mining, “Wikipedia”
3. http://phoenix.phys.clemson.edu/tutorials/excel/regression.html
4. http://wwwmaths.anu.edu.au/~steve/pdcn.pdf
5. Data Mining and Knowledge Discovery
6. www.KDnuggets.com