El capítulo 1 de 'Data Mining: Concepts and Techniques' introduce el concepto de minería de datos, justificando su necesidad debido al crecimiento explosivo de datos y la automatización en su recolección. Se describen las técnicas de minería de datos, funciones como la clasificación y análisis de patrones, y se destacan las aplicaciones en inteligencia empresarial, análisis de mercado y detección de fraudes. Se abordan también los desafíos del sector, como la metodología de minería y la evaluación de patrones interesantes.

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Data Mining: Concepts and
Techniques 1
Chp-1: Introduction to
Data Mining

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Techniques 2
Chapter 1. Introduction
 Motivation: Why data mining?
 What is data mining?
 Data Mining: On what kind of data?
 Kind of patterns to be mined
 Technologies used
 Major issues in data mining

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Why Data Mining?
 The Explosive Growth of Data: from terabytes to petabytes(1000
terabytes)

Data collection and data availability

Automated data collection tools, database systems, Web,
computerized society

Major sources of abundant data

Business: Web, e-commerce, transactions, stocks, …

Science: Remote sensing, bioinformatics, scientific simulation, …

Society and everyone: news, digital cameras, YouTube
 We are drowning in data, but starving for knowledge!
 “Necessity is the mother of invention”—Data mining—Automated
analysis of massive data sets

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Evolution of Database Technology
 1960s:
 Data collection, database creation and network DBMS
 1970s:
 Relational data model, relational DBMS implementation
 1980s:

RDBMS, advanced data models (extended-relational, OO, deductive, etc.)
 Application-oriented DBMS (spatial, scientific, engineering, etc.)
 1990s:
 Data mining, data warehousing, multimedia databases, and Web databases
 2000s

Stream data management and mining
 Data mining and its applications

Web technology (XML, data integration) and global information systems

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Evolution of database system technology

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What Is Data Mining?
 Data mining (knowledge discovery from data)
 Extraction of interesting (non-trivial, implicit, previously
unknown and potentially useful) patterns or knowledge
from huge amount of data
 Data mining: a misnomer?
 Alternative names
 Knowledge discovery (mining) in databases (KDD),
knowledge extraction, data/pattern analysis, data
archeology, data dredging, information harvesting,
business intelligence, etc.

7. Decision making
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Knowledge Discovery (KDD) Process
 Data mining—core of
knowledge discovery
process
Data Cleaning
Data Integration
Databases
Data
Warehouse
Selection & Transformation
Data Mining
Pattern Evaluation

9. 9
Data Mining in Business Intelligence
Increasing potential
to support
business decisions End User
Business
Analyst
Data
Analyst
DBA
Decision
Making
Data Presentation
Visualization Techniques
Data Mining
Information Discovery
Data Exploration
Statistical Summary, Querying, and Reporting
Data Preprocessing/Integration, Data Warehouses
Data Sources
Paper, Files, Web documents, Scientific experiments, Database Systems

10. Why Data Mining?—Potential Applications
 Data analysis and decision support

Market analysis and management

Target marketing, customer relationship
management (CRM), market basket analysis, cross
selling
 Risk analysis and management

Forecasting, customer retention, quality control,
competitive analysis
 Fraud detection and detection of unusual
patterns (outliers
 Other Applications

Text mining (news group, email, documents) and Web
mining 

Stream data mining

Bioinformatics and bio-data analysis
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11. Ex. 1: Market Analysis and Management
 Where does the data come from?—Credit card
transactions, loyalty cards, discount coupons,
customer complaint calls, plus (public) lifestyle studies
 Target marketing
 Find clusters of “model” customers who share the same
characteristics: interest, income level, spending habits, etc.

Determine customer purchasing patterns over time
 Cross-market analysis—Find associations/co-relations
between product sales, & predict based on such
association
 Customer profiling—What types of customers buy
what products (clustering or classification)
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12. Ex. 1: Market Analysis and Management
 Customer requirement analysis

Identify the best products for different groups of customers

Predict what factors will attract new customers
 Provision of summary information

Multidimensional summary reports

Statistical summary information (data central tendency and
variation)
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Data Mining: On What Kinds of Data?
 Database-oriented data sets and applications
 Relational database, data warehouse, transactional database
 Advanced data sets and advanced applications
 Data streams and sensor data
 Time-series data, temporal data, sequence data (incl. bio-sequences)
 Structure data, graphs, social networks and multi-linked data
 Object-relational databases
 Heterogeneous databases and legacy databases
 Spatial data and spatiotemporal data(geographical data)
 Multimedia database
 Text databases
 The World-Wide Web

14. Data Mining: On What Kinds of Data?
 Mining relational databases

Eg. Anaylze customer data to predict the credit risk
of new customers based on their income, age and
previous credit information.
 Data Warehouses

Sales per item type per branch for third quarter.

Data stored to provide information from historical
perespective. Eg. In past 6 to 12 months,
summarized data

Modeled by multidimentional data structure called
data cube.
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16.  Transactional data

Eg analyze which items are sold well together?

Printers are normally purchased together with
computer
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Data Mining: On What Kinds of Data?

17. Kinds of Patterns to be mined
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18. What Kinds of Patterns Can Be Mined?
1) Generalization
2) Association and Correlation Analysis
3) Classification
4) Cluster Analysis
5) Outlier Analysis
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19. Data Mining Function: (1) Generalization
 Multidimensional concept description:
Characterization and discrimination
 Generalize, summarize, and contrast data
characteristics, e.g., summarize the characteristics
of customers who spend more than Rs. 50,000 a
year at an electronics store

Data characterization is a summarization of the
general characteristics or features of a target class
of data
 Data cube technology for computing

OLAP (online analytical processing)
 Examples of Output forms : pie charts, MDD
cubes, bar charts, curves etc.
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20. Data Mining Function: (1) Generalization
contd.
 Data discrimination is a comparison of the general
features of the target class data objects against the
general features of objects from one or multiple
contrasting classes.

Eg. Compare 2 groups of customers- those who shop
for computer products regularly(more than twice a
month) and those who rarely shop for such
products(less than 3 times a year)
 Data cube technology for computing

Drill down on any dimension

Discriminant rules: Discrimination descriptions
expressed in the form of rules
 Output forms : same as that of data characterization
along with discrimination descriptions
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21. Data Mining Function: (2) Association and
Correlation Analysis
 Frequent patterns (or frequent itemsets)

What items are frequently purchased together in
your mart? Eg. Milk & bread
 Association, correlation vs. causality

A typical association rule
 Computer software [1%, 50%] (support,
→
confidence)
 Confidence means that if one buys a computer there is a 50%
chance that she will buy software too. A 1% support means
that 1% of all transactions under analysis show that computer
& software are purchased together
 Association rules are discarded as uninteresting if they
do not satisfy both a minimum support threshold
and a minimum confidence threshold
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22. Data Mining Function: (3) Classification
 Classification and label prediction
 Construct models (functions) based on some training examples

Describe and distinguish classes or concepts for future prediction

E.g., classify countries based on (climate), or classify cars
based on (gas mileage)

Predict some unknown class labels
 Typical methods

Decision trees, naïve Bayesian classification, support vector
machines, neural networks, rule-based classification, pattern-
based classification, logistic regression, …
 Typical applications:

Credit card fraud detection, direct marketing, classifying stars,
diseases, web-pages, …
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23. Various forms of a classification model
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24. Data Mining Function: (4) Cluster Analysis
 Unsupervised learning (i.e., Class label is
unknown)
 Group data to form new categories (i.e.,
clusters), e.g., cluster houses to find
distribution patterns
 Data objects are clustered or grouped based
on the principle of maximizing intraclass
similarity and minimizing interclass similarity
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25. Data Mining Function: (4) Cluster Analysis
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26. Data Mining Function: (5) Outlier Analysis
 Outlier analysis (anomaly mining)
 Outlier: A data object that does not comply
with the general behaviour of the data
 Noise or exception? ― One person’s garbage
could be another person’s treasure
 Methods: by product of clustering or
regression analysis, …
 Useful in fraud detection, rare events analysis
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Are All the “Discovered” Patterns Interesting?
 Data mining may generate thousands of patterns: Not all of them
are interesting

Suggested approach: Human-centered, query-based, focused mining
 Interestingness measures
 A pattern is interesting if it is easily understood by humans, valid on
new or test data with some degree of certainty, potentially useful,
novel, or validates some hypothesis that a user seeks to confirm
 Objective vs. subjective interestingness measures

Objective: based on statistics and structures of patterns, e.g.,
support, confidence, etc.

Subjective: based on user’s belief in the data, e.g. large earthquake
often follows a cluster of small earthquake.

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Find All and Only Interesting Patterns?
 Find all the interesting patterns: Completeness
 Can a data mining system find all the interesting patterns? Do
we need to find all of the interesting patterns?
 Association vs. classification vs. clustering
 Search for only interesting patterns: An optimization problem
 Can a data mining system find only the interesting patterns?
 Approaches

First generate all the patterns and then filter out the
uninteresting ones

Generate only the interesting

29. Technologies Used
 As a highly application-driven domain, data mining has
incorporated many techniques from other domains
 The interdisciplinary nature of data mining research and
development contributes significantly to the success of data
mining and its extensive applications
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Data Mining: Confluence of Multiple Disciplines
Data Mining
Machine
Learning
Statistics
Applications
Algorithm
Pattern
Recognition
High-Performance
Computing
Visualization
Database
Technology

31. Data Mining: Confluence of Multiple Disciplines
 Statistics

Statistical models are widely used to model data and
data classes.

Eg. We can use statistics to model noise and
missing data.
 Machine learning

Computer programs automatically learn to
recognize complex patterns and make intelligent
decisions based on data.

e.g. Handwritten postal codes
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Why Confluence of Multiple Disciplines?
 Tremendous amount of data
 Algorithms must be highly scalable to handle such as tera-bytes
of data
 High-dimensionality of data
 Micro-array may have tens of thousands of dimensions
 High complexity of data
 Data streams and sensor data
 Time-series data, temporal data, sequence data
 Structure data, graphs, social networks and multi-linked data
 Heterogeneous databases and legacy databases
 Spatial, spatiotemporal, multimedia, text and Web data
 Software programs, scientific simulations
 New and sophisticated applications

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Major Issues in Data Mining
 Mining methodology
 Mining different kinds of knowledge from diverse data types,
e.g., files in pdf or doc
 Mining knowledge in multi-dimensional space.
 Data mining: An interdisciplinary effort( mine data with NL
text)
 Pattern evaluation: the interestingness problem
 Handling noise, uncertainty, and incompleteness of data
 Integration of the discovered knowledge with existing one:
knowledge fusion
 Pattern evaluation and pattern- or constraint-guided mining

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Major Issues in Data Mining (1)
 User interaction

Interactive mining( dynamically change focus of search)
 Incorporation of background knowledge(constraints, rules)
 presentation and visualization of data mining results
 Efficiency and Scalability
 Efficiency and scalability of data mining algorithms(run time …
predictable,short,acceptable)
 Parallel, distributed, stream, and incremental mining methods
 Diversity of data types

Handling complex types of data(simple to temporal data
objects)
 Mining dynamic, networked, and global data repositories

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Major Issues in Data Mining (2)
 Data mining and society
 Social impacts of data mining(benefit to society)
 Privacy-preserving data mining
 Invisible data mining(system have buit in function.. click of
mouse)

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Architecture: Typical Data Mining System
data cleaning, integration, and selection
Database or Data
Warehouse Server
Data Mining Engine
Pattern Evaluation
Graphical User Interface
Know
ledge
-Base
Database
Data
Warehouse
World-Wide
Web
Other Info
Repositories

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Techniques 37
Summary
 Data mining: Discovering interesting patterns from large amounts
of data
 A natural evolution of database technology, in great demand, with
wide applications
 A KDD process includes data cleaning, data integration, data
selection, transformation, data mining, pattern evaluation, and
knowledge presentation
 Mining can be performed in a variety of information repositories
 Data mining functionalities: characterization, discrimination,
association, classification, clustering, outlier and trend analysis, etc.
 Data mining systems and architectures
 Major issues in data mining