Introduction to Data Mining and Knowledge DiscoveryChapter 01

Data Mining and Knowledge
Discovery
Chapter 1
Introduction
26-May-24 1

References
Introduction To Data Mining
Second Edition
By Pang-ning Tan, Michael Steinbach, Anuj Karpatne & Vipin Kumar
26-May-24 2

Large-scale Data is Everywhere!
▪ There has been enormous data growth in both
commercial and scientific databases due to
advances in data generation and collection
technologies
▪ New mantra
▪ Gather whatever data you can whenever and
wherever possible.
▪ Expectations
▪ Gathered data will have value either for the
purpose collected or for a purpose not
envisioned.
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Computational
Simulations
Social Networking:
Twitter
Sensor
Networks
Traffic Patterns
Cyber Security

Why Data Mining? Commercial Viewpoint
● Lots of data is being collected
and warehoused
– Web data
◆Google has Peta(=1000 TB) Bytes of web data
◆Facebook has billions of active users
– purchases at department/
grocery stores, e-commerce
◆ Amazon handles millions of visits/day
– Bank/Credit Card transactions
● Computers have become cheaper and more
powerful
● Competitive Pressure is Strong
– Provide better, customized services for an edge (e.g. in
Customer Relationship Management)
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Why Data Mining? Scientific Viewpoint
● Data collected and stored at
enormous speeds
– remote sensors on a satellite
◆ NASA EOSDIS archives over
petabytes of earth science data / year
– telescopes scanning the skies
◆ Sky survey data
– High-throughput biological data
– scientific simulations
◆ terabytes of data generated in a few hours
● Data mining helps scientists
– in automated analysis of massive datasets
– In hypothesis formation
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fMRI Data from Brain Sky Survey Data
Gene Expression Data
Surface Temperature of Earth

Great opportunities to improve productivity in all
walks of life
Big data: The next frontier for innovation,
competition, and productivity
McKinsey Global Institute
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Great Opportunities to Solve Society’s Major Problems
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Improving health care and reducing costs
Finding alternative/ green energy sources
Predicting the impact of climate change
Reducing hunger and poverty by
increasing agriculture production

What is Data Mining?
● Process of automatically discovering useful information in
large data repositories.
● Data mining techniques are deployed to scour large data
sets in order to find novel and useful patterns that might
otherwise remain unknown.
● They also provide the capability to predict the outcome of
a future observation, such as the amount a customer will
spend at an online or a brick-and-mortar store.
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What is Data Mining?...
● Not all information discovery tasks are considered to be data
mining. Examples include queries, e.g., looking up individual
records in a database or finding web pages that contain a
particular set of keywords. This is because such tasks can be
accomplished through simple interactions with a database
management system or an information retrieval system.
● Nonetheless, data mining techniques have been used to enhance
the performance of such systems by improving the quality of
the search results based on their relevance to the input queries.
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Data Mining and KDD
● KDD stands for Knowledge Discovery in Databases.
● Data mining is an integral part of knowledge discovery in
databases (KDD), which is the overall process of converting
raw data into useful information.
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Data Mining and KDD…
● The input data can be stored in a variety of formats (flat files,
spreadsheets, or relational tables) and may reside in a
centralized data repository or be distributed across multiple
sites.
● The purpose of preprocessing is to transform the raw input
data into an appropriate format for subsequent analysis.
– The steps involved in data preprocessing include fusing data
from multiple sources, cleaning data to remove noise and
duplicate observations, and selecting records and features that
are relevant to the data mining task at hand.
• Because of the many ways data can be collected and stored,
data preprocessing is perhaps the most laborious and time -
consuming step in the overall knowledge discovery process.
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Data Mining and KDD…
Postprocessing step ensures that only valid and useful
results are incorporated into the decision support system.
An example of postprocessing is visualization, which allows
analysts to explore the data and the data mining results
from a variety of viewpoints.
Hypothesis testing methods can also be applied during
postprocessing to eliminate spurious data mining results.
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Motivating Challenges
● Scalability: Because of advances in data generation
and collection, data sets with sizes of terabytes, petabytes,
or even exabytes are becoming common. If data mining
algorithms are to handle these massive data sets, they
must be scalable
● High Dimensionality: It is now common to
encounter data sets with hundreds or thousands of
attributes instead of the handful common a few decades
ago. In bioinformatics, progress in microarray technology
has produced gene expression data involving thousands of
features. Data sets with temporal or spatial components
also tend to have high dimensionality.
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● Heterogeneous and Complex Data: Traditional
data analysis methods often deal with data sets containing
attributes of the same type, either continuous or
categorical. As the role of data mining in business, science,
medicine, and other ﬁelds has grown, so has the need for
techniques that can handle heterogeneous attributes.
Recent years have also seen the emergence of more
complex data objects.
● Data Ownership and Distribution: Sometimes,
the data needed for an analysis is not stored in one
location or owned by one organization. Instead, the data is
geographically distributed among resources belonging to
multiple entities. This requires the development of
distributed data mining techniques.
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● Non-traditional Analysis: The traditional
statistical approach is based on a hypothesize-and-test
paradigm. In other words, a hypothesis is proposed, an
experiment is designed to gather the data, and then the
data is analyzed with respect to the hypothesis.
Unfortunately, this process is extremely labor-intensive.
Current data analysis tasks often require the generation
and evaluation of thousands of hypotheses, and
consequently, the development of some data mining
techniques has been motivated by the desire to automate
the process of hypothesis generation and evaluation.
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Origins of Data Mining
● Draws ideas from machine learning/AI, pattern
recognition, statistics, and database systems
● Traditional techniques may be unsuitable due to data that
is
– Large-scale
– High dimensional
– Heterogeneous
– Complex
– Distributed
● A key component of the emerging field of data science and
data-driven discovery
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Is data mining same as machine learning?
• Data mining is designed to extract the rules from large
quantities of data, while machine learning teaches a
computer how to learn and comprehend the given
parameters.
• Data mining is simply a method of researching to
determine a particular outcome based on the total of the
gathered data. On the other side of the coin, we have
machine learning, which trains a system to perform
complex tasks and uses harvested data and experience to
become smarter.
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Data Mining Tasks
• Prediction Methods
• Description Methods
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Data Mining Tasks…
● Prediction Methods
– Use some variables to predict unknown or future
values of other variables.
– The objective of these tasks is to predict the value of a
particular attribute based on the values of other
attributes.
– The attribute to be predicted is commonly known as
the target or dependent variable, while the
attributes used for making the prediction are known as
the explanatory or independent variables.
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From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996

Data Mining Tasks…
● Description Methods
– Find human-interpretable patterns that describe the
data.
– Here, the objective is to derive patterns (correlations,
trends, clusters, trajectories, and anomalies) that
summarize the underlying relationships in data.
– Descriptive data mining tasks are often exploratory in
nature and frequently require postprocessing
techniques to validate and explain the results.
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Milk
Data
Data Mining Tasks …
Four of the core data mining tasks
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Predictive Modeling
● Find a model for class attribute as a function of
the values of other attributes
● There are two types of predictive modeling tasks:
– classification, which is used for discrete target
variables.
– regression, which is used for continuous target
variables.
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Predictive Modeling …
● For example, predicting whether a web user will make
a purchase at an online bookstore is a classification task
because the target variable is binary-valued.
● On the other hand, forecasting the future price of a
stock is a regression task because price is a continuous-
valued attribute.
● The goal of both tasks is to learn a model that
minimizes the error between the predicted and true
values of the target variable.
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Predictive Modeling: Classification
● Find a model for class attribute as a function of
the values of other attributes
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Model for predicting
credit worthiness
Class

Classification Example
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Test
Set
Training
Set
Mode
l
Learn
Classifier

Examples of Classification Task
● Classifying credit card transactions
as legitimate or fraudulent
● Classifying land covers (water bodies, urban areas, forests, etc.) using
satellite data
● Categorizing news stories as finance,
weather, entertainment, sports, etc
● Identifying intruders in the cyberspace
● Predicting tumor cells as benign or malignant
● Classifying secondary structures of protein
as alpha-helix, beta-sheet, or random coil
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Classification: Application 1
● Fraud Detection
– Goal: Predict fraudulent cases in credit card
transactions.
– Approach:
◆ Use credit card transactions and the information on its
account-holder as attributes.
– When does a customer buy, what does he buy,
how often he pays on time, etc
◆ Label past transactions as fraud or fair transactions. This
forms the class attribute.
◆ Learn a model for the class of the transactions.
◆ Use this model to detect fraud by observing credit card
transactions on an account.
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● Churn prediction for telephone customers
– Goal: To predict whether a customer is likely to be
lost to a competitor.
– Approach:
◆ Use detailed record of transactions with each of the past and
present customers, to find attributes.
– How often the customer calls, where he calls, what
time-of-the day he calls most, his financial status,
marital status, etc.
◆ Label the customers as loyal or disloyal.
◆ Find a model for loyalty.
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● Sky Survey Cataloging
– Goal: To predict class (star or galaxy) of sky objects,
especially visually faint ones, based on the telescopic
survey images (from Palomar Observatory).
– 3000 images with 23,040 x 23,040 pixels per image.
– Approach:
◆ Segment the image.
◆ Measure image attributes (features) - 40 of them per object.
◆ Model the class based on these features.
◆ Success Story: Could find 16 new high red-shift quasars, some
of the farthest objects that are difficult to find!
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From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996

Classifying Galaxies
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Early
Intermediate
Late
Data Size:
● 72 million stars, 20 million
galaxies
● Object Catalog: 9 GB
● Image Database: 150 GB
Class:
● Stages of Formation
Attributes:
● Image features,
● Characteristics of light
waves received, etc.
Courtesy: http://aps.umn.edu

Regression
● Predict a value of a given continuous valued variable
based on the values of other variables, assuming a linear
or nonlinear model of dependency.
● Extensively studied in statistics, neural network fields.
● Examples:
– Predicting sales amounts of new product based on
advertising expenditure.
– Predicting wind velocities as a function of temperature,
humidity, air pressure, etc.
– Time series prediction of stock market indices.
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Clustering
● Finding groups of objects such that the objects in a group will be
similar (or related) to one another and different from (or unrelated
to) the objects in other groups.
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Inter-cluster
distances are
maximized
Intra-cluster
distances are
minimized

Applications of Cluster Analysis
● Understanding
– Custom profiling for targeted marketing
– Group related documents for browsing
– Group genes and proteins that have
similar functionality
– Group stocks with similar price
fluctuations
● Summarization
– Reduce the size of large data sets
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Use of K-means to
partition Sea Surface
Temperature (SST) and
Net Primary Production
(NPP) into clusters that
reflect the Northern
and Southern
Hemispheres.
Courtesy: Michael Eisen

Clustering: Application 1
● 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.
– Approach:
◆ Collect different attributes of customers based on their
geographical and lifestyle related information.
◆ Find clusters of similar customers.
◆ Measure the clustering quality by observing buying patterns of
customers in same cluster vs. those from different clusters.
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Clustering: Application 2
● Document Clustering:
– Goal: To find groups of documents that are similar to
each other based on the important terms appearing in
them.
– Approach: To identify frequently occurring terms in
each document. Form a similarity measure based on
the frequencies of different terms. Use it to cluster.
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Enron email dataset

Association Rule Discovery: Definition
● Given a set of records each of which contain some
number of items from a given collection
– Produce dependency rules which will predict
occurrence of an item based on occurrences of other
items.
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Rules Discovered:
{Diapers}→{Milk}

Association Analysis: Applications
● Market-basket analysis
– Rules are used for sales promotion, shelf management,
and inventory management
● Telecommunication alarm diagnosis
– Rules are used to find combination of alarms that
occur together frequently in the same time period
● Medical Informatics
– Rules are used to find combination of patient
symptoms and test results associated with certain
diseases
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Deviation/Anomaly/Change Detection
● Detect significant deviations from normal behavior
● Applications:
– Credit Card Fraud Detection
– Network Intrusion
Detection
– Identify anomalous behavior from sensor networks for monitoring and
surveillance.
– Detecting changes in the global forest cover.
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Introduction to Data Mining and Knowledge DiscoveryChapter 01

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