SlideShare a Scribd company logo
1 of 23
Download to read offline
6months industrial training in data mining,ludhiana
INTRODUCTION
 Motivation: Why data mining?
 What is data mining?
 Data Mining: On what kind of data?
 Data mining functionality
 Are all the patterns interesting?
 Classification of data mining systems
 Major issues in data mining
MOTIVATION: “NECESSITY IS THE
MOTHER OF INVENTION”
 Data explosion problem
 Automated data collection tools and mature database
technology lead to tremendous amounts of data stored in
databases, data warehouses and other information
repositories
 We are drowning in data, but starving for knowledge!
 Solution: Data warehousing and data mining
 Data warehousing and on-line analytical processing
 Extraction of interesting knowledge (rules, regularities,
patterns, constraints) from data in large databases
EVOLUTION OF DATABASE
TECHNOLOGY
 1960s:
 Data collection, database creation, IMS and network DBMS
 1970s:
 Relational data model, relational DBMS implementation
 1980s:
 RDBMS, advanced data models (extended-relational, OO,
deductive, etc.) and application-oriented DBMS (spatial,
scientific, engineering, etc.)
 1990s—2000s:
 Data mining and data warehousing, multimedia databases,
and Web databases
WHAT IS DATA MINING?
 Data mining (knowledge discovery in databases):
 Extraction of interesting (non-trivial, implicit, previously
unknown and potentially useful) information or patterns
from data in large databases
 Alternative names
 Knowledge discovery(mining) in databases (KDD),
knowledge extraction, data/pattern analysis, data
archeology, data dredging, information harvesting,
business intelligence, etc.
 What is not data mining?
 (Deductive) query processing.
 Expert systems or small ML/statistical programs
WHY DATA MINING? — POTENTIAL
APPLICATIONS
 Database analysis and decision support
 Market analysis and management
 target marketing, customer relation management,
market basket analysis, cross selling, market
segmentation
 Risk analysis and management
 Forecasting, customer retention, improved underwriting,
quality control, competitive analysis
 Fraud detection and management
 Other Applications
 Text mining (news group, email, documents) and Web analysis.
 Intelligent query answering
MARKET ANALYSIS AND MANAGEMENT
(1)
 Where are the data sources for analysis?
 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
 Conversion of single to a joint bank account: marriage, etc.
 Cross-market analysis
 Associations/co-relations between product sales
 Prediction based on the association information
MARKET ANALYSIS AND MANAGEMENT
(2)
 Customer profiling
 data mining can tell you what types of customers buy what
products (clustering or classification)
 Identifying customer requirements
 identifying the best products for different customers
 use prediction to find what factors will attract new customers
 Provides summary information
 various multidimensional summary reports
 statistical summary information (data central tendency and
variation)
CORPORATE ANALYSIS AND RISK
MANAGEMENT
 Finance planning and asset evaluation
 cash flow analysis and prediction
 contingent claim analysis to evaluate assets
 cross-sectional and time series analysis (financial-ratio, trend
analysis, etc.)
 Resource planning:
 summarize and compare the resources and spending
 Competition:
 monitor competitors and market directions
 group customers into classes and a class-based pricing
procedure
 set pricing strategy in a highly competitive market
FRAUD DETECTION AND MANAGEMENT
(1)
 Applications
 widely used in health care, retail, credit card services,
telecommunications (phone card fraud), etc.
 Approach
 use historical data to build models of fraudulent behavior and
use data mining to help identify similar instances
 Examples
 auto insurance: detect a group of people who stage
accidents to collect on insurance
 money laundering: detect suspicious money transactions (US
Treasury's Financial Crimes Enforcement Network)
 medical insurance: detect professional patients and ring of
doctors and ring of references
FRAUD DETECTION AND MANAGEMENT
(2)
 Detecting inappropriate medical treatment
 Australian Health Insurance Commission identifies that in
many cases blanket screening tests were requested (save
Australian $1m/yr).
 Detecting telephone fraud
 Telephone call model: destination of the call, duration,
time of day or week. Analyze patterns that deviate from
an expected norm.
 British Telecom identified discrete groups of callers with
frequent intra-group calls, especially mobile phones, and
broke a multimillion dollar fraud.
 Retail
 Analysts estimate that 38% of retail shrink is due to
dishonest employees.
OTHER APPLICATIONS
 Sports
 IBM Advanced Scout analyzed NBA game statistics (shots
blocked, assists, and fouls) to gain competitive advantage
for New York Knicks and Miami Heat
 Astronomy
 JPL and the Palomar Observatory discovered 22 quasars with
the help of data mining
 Internet Web Surf-Aid
 IBM Surf-Aid applies data mining algorithms to Web access
logs for market-related pages to discover customer
preference and behavior pages, analyzing effectiveness of
Web marketing, improving Web site organization, etc.
DATA MINING: A KDD PROCESS
 Data mining: the core of
knowledge discovery process.
Data Cleaning
Data Integration
Databases
Data
Warehouse
Task-relevant Data
Selection
Data Mining
Pattern Evaluation
STEPS OF A KDD PROCESS
 Learning the application domain:
 relevant prior knowledge and goals of application
 Creating a target data set: data selection
 Data cleaning and preprocessing: (may take 60% of effort!)
 Data reduction and transformation:
 Find useful features, dimensionality/variable reduction, invariant
representation.
 Choosing functions of data mining
 summarization, classification, regression, association, clustering.
 Choosing the mining algorithm(s)
 Data mining: search for patterns of interest
 Pattern evaluation and knowledge presentation
 visualization, transformation, removing redundant patterns, etc.
 Use of discovered knowledge
DATA MINING AND BUSINESS INTELLIGENCE
Increasing potential
to support
business decisions End User
Business
Analyst
Data
Analyst
DBA
Making
Decisions
Data Presentation
Visualization Techniques
Data Mining
Information Discovery
Data Exploration
OLAP, MDA
Statistical Analysis, Querying and Reporting
Data Warehouses / Data Marts
Data Sources
Paper, Files, Information Providers, Database Systems, OLTP
ARCHITECTURE OF A TYPICAL DATA
MINING SYSTEM
Data
Warehouse
Data cleaning & data integration Filtering
Databases
Database or data
warehouse server
Data mining engine
Pattern evaluation
Graphical user interface
Knowledge-base
DATA MINING: ON WHAT KIND OF
DATA?
 Relational databases
 Data warehouses
 Transactional databases
 Advanced DB and information repositories
 Object-oriented and object-relational databases
 Spatial databases
 Time-series data and temporal data
 Text databases and multimedia databases
 Heterogeneous and legacy databases
 WWW
DATA MINING FUNCTIONALITIES
(1)
 Concept description: Characterization and discrimination
 Generalize, summarize, and contrast data characteristics, e.g.,
dry vs. wet regions
 Association (correlation and causality)
 Multi-dimensional vs. single-dimensional association
 age(X, “20..29”) ^ income(X, “20..29K”)  buys(X, “PC”) [support =
2%, confidence = 60%]
 contains(T, “computer”)  contains(x, “software”) [1%, 75%]
DATA MINING FUNCTIONALITIES
(2)
 Classification and Prediction
 Finding models (functions) that describe and distinguish
classes or concepts for future prediction
 E.g., classify countries based on climate, or classify cars based
on gas mileage
 Presentation: decision-tree, classification rule, neural network
 Prediction: Predict some unknown or missing numerical values
 Cluster analysis
 Class label is unknown: Group data to form new classes, e.g.,
cluster houses to find distribution patterns
 Clustering based on the principle: maximizing the intra-class
similarity and minimizing the interclass similarity
DATA MINING FUNCTIONALITIES
(3)
 Outlier analysis
 Outlier: a data object that does not comply with the general
behavior of the data
 It can be considered as noise or exception but is quite useful in
fraud detection, rare events analysis
 Trend and evolution analysis
 Trend and deviation: regression analysis
 Sequential pattern mining, periodicity analysis
 Similarity-based analysis
 Other pattern-directed or statistical analyses
ARE ALL THE “DISCOVERED”
PATTERNS INTERESTING?
 A data mining system/query 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., unexpectedness,
novelty, actionability, etc.
CAN WE FIND ALL AND ONLY
INTERESTING PATTERNS?
 Find all the interesting patterns: Completeness
 Can a data mining system find all the interesting patterns?
 Association vs. classification vs. clustering
 Search for only interesting patterns: Optimization
 Can a data mining system find only the interesting patterns?
 Approaches
 First general all the patterns and then filter out the
uninteresting ones.
 Generate only the interesting patterns—mining query
optimization
DATA MINING: CONFLUENCE OF
MULTIPLE DISCIPLINES
Data Mining
Database
Technology
Statistics
Other
Disciplines
Information
Science
Machine
Learning
Visualization

More Related Content

What's hot

Data Mining Concepts
Data Mining ConceptsData Mining Concepts
Data Mining ConceptsDung Nguyen
 
Additional themes of data mining for Msc CS
Additional themes of data mining for Msc CSAdditional themes of data mining for Msc CS
Additional themes of data mining for Msc CSThanveen
 
Introduction to Data Mining
Introduction to Data Mining Introduction to Data Mining
Introduction to Data Mining Sushil Kulkarni
 
Data Mining & Applications
Data Mining & ApplicationsData Mining & Applications
Data Mining & ApplicationsFazle Rabbi Ador
 
Data mining
Data mining Data mining
Data mining AthiraR23
 
data mining and data warehousing
data mining and data warehousingdata mining and data warehousing
data mining and data warehousingSunny Gandhi
 
Chapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & Kamber
Chapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & KamberChapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & Kamber
Chapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & Kambererror007
 
MC0088 Internal Assignment (SMU)
MC0088 Internal Assignment (SMU)MC0088 Internal Assignment (SMU)
MC0088 Internal Assignment (SMU)Krishan Pareek
 
Chapter - 8.2 Data Mining Concepts and Techniques 2nd Ed slides Han & Kamber
Chapter - 8.2 Data Mining Concepts and Techniques 2nd Ed slides Han & KamberChapter - 8.2 Data Mining Concepts and Techniques 2nd Ed slides Han & Kamber
Chapter - 8.2 Data Mining Concepts and Techniques 2nd Ed slides Han & Kambererror007
 
Data mining (lecture 1 & 2) conecpts and techniques
Data mining (lecture 1 & 2) conecpts and techniquesData mining (lecture 1 & 2) conecpts and techniques
Data mining (lecture 1 & 2) conecpts and techniquesSaif Ullah
 
Introduction to Data mining
Introduction to Data miningIntroduction to Data mining
Introduction to Data miningHadi Fadlallah
 

What's hot (15)

Data Mining Concepts
Data Mining ConceptsData Mining Concepts
Data Mining Concepts
 
Additional themes of data mining for Msc CS
Additional themes of data mining for Msc CSAdditional themes of data mining for Msc CS
Additional themes of data mining for Msc CS
 
3 Data Mining Tasks
3  Data Mining Tasks3  Data Mining Tasks
3 Data Mining Tasks
 
Introduction to Data Mining
Introduction to Data Mining Introduction to Data Mining
Introduction to Data Mining
 
Data Mining & Applications
Data Mining & ApplicationsData Mining & Applications
Data Mining & Applications
 
Data mining
Data mining Data mining
Data mining
 
data mining and data warehousing
data mining and data warehousingdata mining and data warehousing
data mining and data warehousing
 
Data mining
Data miningData mining
Data mining
 
Chapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & Kamber
Chapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & KamberChapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & Kamber
Chapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & Kamber
 
MC0088 Internal Assignment (SMU)
MC0088 Internal Assignment (SMU)MC0088 Internal Assignment (SMU)
MC0088 Internal Assignment (SMU)
 
Chapter - 8.2 Data Mining Concepts and Techniques 2nd Ed slides Han & Kamber
Chapter - 8.2 Data Mining Concepts and Techniques 2nd Ed slides Han & KamberChapter - 8.2 Data Mining Concepts and Techniques 2nd Ed slides Han & Kamber
Chapter - 8.2 Data Mining Concepts and Techniques 2nd Ed slides Han & Kamber
 
Data mining
Data miningData mining
Data mining
 
Data Mining
Data MiningData Mining
Data Mining
 
Data mining (lecture 1 & 2) conecpts and techniques
Data mining (lecture 1 & 2) conecpts and techniquesData mining (lecture 1 & 2) conecpts and techniques
Data mining (lecture 1 & 2) conecpts and techniques
 
Introduction to Data mining
Introduction to Data miningIntroduction to Data mining
Introduction to Data mining
 

Viewers also liked

Matlab final year project in ludhiana
Matlab final year project in ludhianaMatlab final year project in ludhiana
Matlab final year project in ludhianadeepikakaler1
 
6months industrial training in image processing, jalandhar
6months industrial training in image processing, jalandhar6months industrial training in image processing, jalandhar
6months industrial training in image processing, jalandhardeepikakaler1
 
6months industrial training in fuzzy logic, jalandhar
6months industrial training in fuzzy logic, jalandhar6months industrial training in fuzzy logic, jalandhar
6months industrial training in fuzzy logic, jalandhardeepikakaler1
 
Artificial intelligence mtech project in ludhiana
Artificial intelligence mtech project in ludhiana Artificial intelligence mtech project in ludhiana
Artificial intelligence mtech project in ludhiana deepikakaler1
 
6 weeks summer training in data mining,ludhiana
6 weeks summer training in data mining,ludhiana6 weeks summer training in data mining,ludhiana
6 weeks summer training in data mining,ludhianadeepikakaler1
 
6 weeks summer training in .Net6,jalandhar
6 weeks summer training in .Net6,jalandhar6 weeks summer training in .Net6,jalandhar
6 weeks summer training in .Net6,jalandhardeepikakaler1
 
image processing project course ludhiana
image processing project course ludhianaimage processing project course ludhiana
image processing project course ludhianadeepikakaler1
 
6 weeks summer training in software testing,jalandhar
6 weeks summer training in software testing,jalandhar6 weeks summer training in software testing,jalandhar
6 weeks summer training in software testing,jalandhardeepikakaler1
 
6months industrial training in embedded, jalandhar
6months industrial training in embedded, jalandhar6months industrial training in embedded, jalandhar
6months industrial training in embedded, jalandhardeepikakaler1
 
Software testing mtech project in jalandhar
Software testing mtech project in jalandharSoftware testing mtech project in jalandhar
Software testing mtech project in jalandhardeepikakaler1
 
6months industrial training in labview, ludhiana
6months industrial training in labview, ludhiana6months industrial training in labview, ludhiana
6months industrial training in labview, ludhianadeepikakaler1
 
6 weeks summer training in fuzzy logic,jalandhar
6 weeks summer training in fuzzy logic,jalandhar6 weeks summer training in fuzzy logic,jalandhar
6 weeks summer training in fuzzy logic,jalandhardeepikakaler1
 
Hfss final year project in jalandhar
Hfss final year project in jalandharHfss final year project in jalandhar
Hfss final year project in jalandhardeepikakaler1
 
6 weeks summer training in hfss,ludhiana
6 weeks summer training in hfss,ludhiana6 weeks summer training in hfss,ludhiana
6 weeks summer training in hfss,ludhianadeepikakaler1
 
6 weeks summer training in data mining,jalandhar
6 weeks summer training in data mining,jalandhar6 weeks summer training in data mining,jalandhar
6 weeks summer training in data mining,jalandhardeepikakaler1
 
Artificial intelligence project in jalandhar
Artificial intelligence project in jalandharArtificial intelligence project in jalandhar
Artificial intelligence project in jalandhardeepikakaler1
 
6months industrial training in hfss, jalandhar
6months industrial training in hfss, jalandhar6months industrial training in hfss, jalandhar
6months industrial training in hfss, jalandhardeepikakaler1
 

Viewers also liked (17)

Matlab final year project in ludhiana
Matlab final year project in ludhianaMatlab final year project in ludhiana
Matlab final year project in ludhiana
 
6months industrial training in image processing, jalandhar
6months industrial training in image processing, jalandhar6months industrial training in image processing, jalandhar
6months industrial training in image processing, jalandhar
 
6months industrial training in fuzzy logic, jalandhar
6months industrial training in fuzzy logic, jalandhar6months industrial training in fuzzy logic, jalandhar
6months industrial training in fuzzy logic, jalandhar
 
Artificial intelligence mtech project in ludhiana
Artificial intelligence mtech project in ludhiana Artificial intelligence mtech project in ludhiana
Artificial intelligence mtech project in ludhiana
 
6 weeks summer training in data mining,ludhiana
6 weeks summer training in data mining,ludhiana6 weeks summer training in data mining,ludhiana
6 weeks summer training in data mining,ludhiana
 
6 weeks summer training in .Net6,jalandhar
6 weeks summer training in .Net6,jalandhar6 weeks summer training in .Net6,jalandhar
6 weeks summer training in .Net6,jalandhar
 
image processing project course ludhiana
image processing project course ludhianaimage processing project course ludhiana
image processing project course ludhiana
 
6 weeks summer training in software testing,jalandhar
6 weeks summer training in software testing,jalandhar6 weeks summer training in software testing,jalandhar
6 weeks summer training in software testing,jalandhar
 
6months industrial training in embedded, jalandhar
6months industrial training in embedded, jalandhar6months industrial training in embedded, jalandhar
6months industrial training in embedded, jalandhar
 
Software testing mtech project in jalandhar
Software testing mtech project in jalandharSoftware testing mtech project in jalandhar
Software testing mtech project in jalandhar
 
6months industrial training in labview, ludhiana
6months industrial training in labview, ludhiana6months industrial training in labview, ludhiana
6months industrial training in labview, ludhiana
 
6 weeks summer training in fuzzy logic,jalandhar
6 weeks summer training in fuzzy logic,jalandhar6 weeks summer training in fuzzy logic,jalandhar
6 weeks summer training in fuzzy logic,jalandhar
 
Hfss final year project in jalandhar
Hfss final year project in jalandharHfss final year project in jalandhar
Hfss final year project in jalandhar
 
6 weeks summer training in hfss,ludhiana
6 weeks summer training in hfss,ludhiana6 weeks summer training in hfss,ludhiana
6 weeks summer training in hfss,ludhiana
 
6 weeks summer training in data mining,jalandhar
6 weeks summer training in data mining,jalandhar6 weeks summer training in data mining,jalandhar
6 weeks summer training in data mining,jalandhar
 
Artificial intelligence project in jalandhar
Artificial intelligence project in jalandharArtificial intelligence project in jalandhar
Artificial intelligence project in jalandhar
 
6months industrial training in hfss, jalandhar
6months industrial training in hfss, jalandhar6months industrial training in hfss, jalandhar
6months industrial training in hfss, jalandhar
 

Similar to 6months industrial training in data mining,ludhiana

Data mining final year project in ludhiana
Data mining final year project in ludhianaData mining final year project in ludhiana
Data mining final year project in ludhianadeepikakaler1
 
Data mining final year project in jalandhar
Data mining final year project in jalandharData mining final year project in jalandhar
Data mining final year project in jalandhardeepikakaler1
 
Data warehouse and data mining
Data warehouse and data miningData warehouse and data mining
Data warehouse and data miningRohit Kumar
 
Lect 1 introduction
Lect 1 introductionLect 1 introduction
Lect 1 introductionhktripathy
 
Introduction.ppt
Introduction.pptIntroduction.ppt
Introduction.pptbommaiah
 
Lect 1 introduction
Lect 1 introductionLect 1 introduction
Lect 1 introductionhktripathy
 
Introduction To Data Mining
Introduction To Data Mining   Introduction To Data Mining
Introduction To Data Mining Phi Jack
 
What Is DATA MINING(INTRODUCTION)
What Is DATA MINING(INTRODUCTION)What Is DATA MINING(INTRODUCTION)
What Is DATA MINING(INTRODUCTION)Pratik Tambekar
 
Data mining and knowledge discovery
Data mining and knowledge discoveryData mining and knowledge discovery
Data mining and knowledge discoveryYoung Alista
 
Data mining and knowledge discovery
Data mining and knowledge discoveryData mining and knowledge discovery
Data mining and knowledge discoveryHarry Potter
 
Data mining and knowledge discovery
Data mining and knowledge discoveryData mining and knowledge discovery
Data mining and knowledge discoveryJames Wong
 
Data mining and knowledge discovery
Data mining and knowledge discoveryData mining and knowledge discovery
Data mining and knowledge discoveryFraboni Ec
 
Data mining and knowledge discovery
Data mining and knowledge discoveryData mining and knowledge discovery
Data mining and knowledge discoveryLuis Goldster
 
Data mining and knowledge discovery
Data mining and knowledge discoveryData mining and knowledge discovery
Data mining and knowledge discoveryTony Nguyen
 
Data mining and knowledge discovery
Data mining and knowledge discoveryData mining and knowledge discovery
Data mining and knowledge discoveryHoang Nguyen
 
Data Mining Xuequn Shang NorthWestern Polytechnical University
Data Mining Xuequn Shang NorthWestern Polytechnical UniversityData Mining Xuequn Shang NorthWestern Polytechnical University
Data Mining Xuequn Shang NorthWestern Polytechnical Universitybutest
 
Data mining 1 - Introduction (cheat sheet - printable)
Data mining 1 - Introduction (cheat sheet - printable)Data mining 1 - Introduction (cheat sheet - printable)
Data mining 1 - Introduction (cheat sheet - printable)yesheeka
 

Similar to 6months industrial training in data mining,ludhiana (20)

Data mining final year project in ludhiana
Data mining final year project in ludhianaData mining final year project in ludhiana
Data mining final year project in ludhiana
 
Data mining final year project in jalandhar
Data mining final year project in jalandharData mining final year project in jalandhar
Data mining final year project in jalandhar
 
Data warehouse and data mining
Data warehouse and data miningData warehouse and data mining
Data warehouse and data mining
 
Data mining
Data miningData mining
Data mining
 
Introduction to data warehouse
Introduction to data warehouseIntroduction to data warehouse
Introduction to data warehouse
 
Lect 1 introduction
Lect 1 introductionLect 1 introduction
Lect 1 introduction
 
Introduction.ppt
Introduction.pptIntroduction.ppt
Introduction.ppt
 
Lect 1 introduction
Lect 1 introductionLect 1 introduction
Lect 1 introduction
 
Introduction To Data Mining
Introduction To Data Mining   Introduction To Data Mining
Introduction To Data Mining
 
What Is DATA MINING(INTRODUCTION)
What Is DATA MINING(INTRODUCTION)What Is DATA MINING(INTRODUCTION)
What Is DATA MINING(INTRODUCTION)
 
Data mining and knowledge discovery
Data mining and knowledge discoveryData mining and knowledge discovery
Data mining and knowledge discovery
 
Data mining and knowledge discovery
Data mining and knowledge discoveryData mining and knowledge discovery
Data mining and knowledge discovery
 
Data mining and knowledge discovery
Data mining and knowledge discoveryData mining and knowledge discovery
Data mining and knowledge discovery
 
Data mining and knowledge discovery
Data mining and knowledge discoveryData mining and knowledge discovery
Data mining and knowledge discovery
 
Data mining and knowledge discovery
Data mining and knowledge discoveryData mining and knowledge discovery
Data mining and knowledge discovery
 
Data mining and knowledge discovery
Data mining and knowledge discoveryData mining and knowledge discovery
Data mining and knowledge discovery
 
Data mining and knowledge discovery
Data mining and knowledge discoveryData mining and knowledge discovery
Data mining and knowledge discovery
 
Dma unit 1
Dma unit   1Dma unit   1
Dma unit 1
 
Data Mining Xuequn Shang NorthWestern Polytechnical University
Data Mining Xuequn Shang NorthWestern Polytechnical UniversityData Mining Xuequn Shang NorthWestern Polytechnical University
Data Mining Xuequn Shang NorthWestern Polytechnical University
 
Data mining 1 - Introduction (cheat sheet - printable)
Data mining 1 - Introduction (cheat sheet - printable)Data mining 1 - Introduction (cheat sheet - printable)
Data mining 1 - Introduction (cheat sheet - printable)
 

More from deepikakaler1

Vlsi final year project in ludhiana
Vlsi final year project in ludhianaVlsi final year project in ludhiana
Vlsi final year project in ludhianadeepikakaler1
 
Software testing mtech project in ludhiana
Software testing mtech project in ludhianaSoftware testing mtech project in ludhiana
Software testing mtech project in ludhianadeepikakaler1
 
Opnet final year project in ludhiana
Opnet final year project in ludhianaOpnet final year project in ludhiana
Opnet final year project in ludhianadeepikakaler1
 
Labview phd project in ludhiana
Labview phd project in ludhianaLabview phd project in ludhiana
Labview phd project in ludhianadeepikakaler1
 
Hfss final year project in ludhiana
Hfss final year project in ludhianaHfss final year project in ludhiana
Hfss final year project in ludhianadeepikakaler1
 
Fuzzy logic mtech project in ludhiana
Fuzzy logic mtech project in ludhiana Fuzzy logic mtech project in ludhiana
Fuzzy logic mtech project in ludhiana deepikakaler1
 
Embedded final year project in ludhiana
Embedded final year project in ludhianaEmbedded final year project in ludhiana
Embedded final year project in ludhianadeepikakaler1
 
Android mtech project in ludhiana
Android  mtech project in ludhianaAndroid  mtech project in ludhiana
Android mtech project in ludhianadeepikakaler1
 
.Net final year project in LUDHIANA
.Net final year project in LUDHIANA.Net final year project in LUDHIANA
.Net final year project in LUDHIANAdeepikakaler1
 
6 weeks/months summer training in vlsi,ludhiana
6 weeks/months summer training in vlsi,ludhiana6 weeks/months summer training in vlsi,ludhiana
6 weeks/months summer training in vlsi,ludhianadeepikakaler1
 
6 weeks summer training in software testing,ludhiana
6 weeks summer training in software testing,ludhiana6 weeks summer training in software testing,ludhiana
6 weeks summer training in software testing,ludhianadeepikakaler1
 
6months industrial training in software testing, ludhiana
6months industrial training in software testing, ludhiana6months industrial training in software testing, ludhiana
6months industrial training in software testing, ludhianadeepikakaler1
 
6 weeks summer training in labview,ludhiana
6 weeks summer training in labview,ludhiana6 weeks summer training in labview,ludhiana
6 weeks summer training in labview,ludhianadeepikakaler1
 
6months industrial training in hfss, ludhiana
6months industrial training in hfss, ludhiana6months industrial training in hfss, ludhiana
6months industrial training in hfss, ludhianadeepikakaler1
 
6 weeks summer training in fuzzy logic,ludhiana
6 weeks summer training in fuzzy logic,ludhiana6 weeks summer training in fuzzy logic,ludhiana
6 weeks summer training in fuzzy logic,ludhianadeepikakaler1
 
6months industrial training in fuzzy logic, ludhiana
6months industrial training in fuzzy logic, ludhiana6months industrial training in fuzzy logic, ludhiana
6months industrial training in fuzzy logic, ludhianadeepikakaler1
 
6months industrial training in embedded, ludhiana
6months industrial training in embedded, ludhiana6months industrial training in embedded, ludhiana
6months industrial training in embedded, ludhianadeepikakaler1
 
6 weeks summer training in embedded,ludhiana
6 weeks summer training in embedded,ludhiana6 weeks summer training in embedded,ludhiana
6 weeks summer training in embedded,ludhianadeepikakaler1
 
6 weeks summer training in android,ludhiana
6 weeks summer training in android,ludhiana6 weeks summer training in android,ludhiana
6 weeks summer training in android,ludhianadeepikakaler1
 
Vlsi final year project in jalandhar
Vlsi final year project in jalandharVlsi final year project in jalandhar
Vlsi final year project in jalandhardeepikakaler1
 

More from deepikakaler1 (20)

Vlsi final year project in ludhiana
Vlsi final year project in ludhianaVlsi final year project in ludhiana
Vlsi final year project in ludhiana
 
Software testing mtech project in ludhiana
Software testing mtech project in ludhianaSoftware testing mtech project in ludhiana
Software testing mtech project in ludhiana
 
Opnet final year project in ludhiana
Opnet final year project in ludhianaOpnet final year project in ludhiana
Opnet final year project in ludhiana
 
Labview phd project in ludhiana
Labview phd project in ludhianaLabview phd project in ludhiana
Labview phd project in ludhiana
 
Hfss final year project in ludhiana
Hfss final year project in ludhianaHfss final year project in ludhiana
Hfss final year project in ludhiana
 
Fuzzy logic mtech project in ludhiana
Fuzzy logic mtech project in ludhiana Fuzzy logic mtech project in ludhiana
Fuzzy logic mtech project in ludhiana
 
Embedded final year project in ludhiana
Embedded final year project in ludhianaEmbedded final year project in ludhiana
Embedded final year project in ludhiana
 
Android mtech project in ludhiana
Android  mtech project in ludhianaAndroid  mtech project in ludhiana
Android mtech project in ludhiana
 
.Net final year project in LUDHIANA
.Net final year project in LUDHIANA.Net final year project in LUDHIANA
.Net final year project in LUDHIANA
 
6 weeks/months summer training in vlsi,ludhiana
6 weeks/months summer training in vlsi,ludhiana6 weeks/months summer training in vlsi,ludhiana
6 weeks/months summer training in vlsi,ludhiana
 
6 weeks summer training in software testing,ludhiana
6 weeks summer training in software testing,ludhiana6 weeks summer training in software testing,ludhiana
6 weeks summer training in software testing,ludhiana
 
6months industrial training in software testing, ludhiana
6months industrial training in software testing, ludhiana6months industrial training in software testing, ludhiana
6months industrial training in software testing, ludhiana
 
6 weeks summer training in labview,ludhiana
6 weeks summer training in labview,ludhiana6 weeks summer training in labview,ludhiana
6 weeks summer training in labview,ludhiana
 
6months industrial training in hfss, ludhiana
6months industrial training in hfss, ludhiana6months industrial training in hfss, ludhiana
6months industrial training in hfss, ludhiana
 
6 weeks summer training in fuzzy logic,ludhiana
6 weeks summer training in fuzzy logic,ludhiana6 weeks summer training in fuzzy logic,ludhiana
6 weeks summer training in fuzzy logic,ludhiana
 
6months industrial training in fuzzy logic, ludhiana
6months industrial training in fuzzy logic, ludhiana6months industrial training in fuzzy logic, ludhiana
6months industrial training in fuzzy logic, ludhiana
 
6months industrial training in embedded, ludhiana
6months industrial training in embedded, ludhiana6months industrial training in embedded, ludhiana
6months industrial training in embedded, ludhiana
 
6 weeks summer training in embedded,ludhiana
6 weeks summer training in embedded,ludhiana6 weeks summer training in embedded,ludhiana
6 weeks summer training in embedded,ludhiana
 
6 weeks summer training in android,ludhiana
6 weeks summer training in android,ludhiana6 weeks summer training in android,ludhiana
6 weeks summer training in android,ludhiana
 
Vlsi final year project in jalandhar
Vlsi final year project in jalandharVlsi final year project in jalandhar
Vlsi final year project in jalandhar
 

Recently uploaded

Easter in the USA presentation by Chloe.
Easter in the USA presentation by Chloe.Easter in the USA presentation by Chloe.
Easter in the USA presentation by Chloe.EnglishCEIPdeSigeiro
 
How to Use api.constrains ( ) in Odoo 17
How to Use api.constrains ( ) in Odoo 17How to Use api.constrains ( ) in Odoo 17
How to Use api.constrains ( ) in Odoo 17Celine George
 
General views of Histopathology and step
General views of Histopathology and stepGeneral views of Histopathology and step
General views of Histopathology and stepobaje godwin sunday
 
Ultra structure and life cycle of Plasmodium.pptx
Ultra structure and life cycle of Plasmodium.pptxUltra structure and life cycle of Plasmodium.pptx
Ultra structure and life cycle of Plasmodium.pptxDr. Asif Anas
 
What is the Future of QuickBooks DeskTop?
What is the Future of QuickBooks DeskTop?What is the Future of QuickBooks DeskTop?
What is the Future of QuickBooks DeskTop?TechSoup
 
How to Make a Field read-only in Odoo 17
How to Make a Field read-only in Odoo 17How to Make a Field read-only in Odoo 17
How to Make a Field read-only in Odoo 17Celine George
 
Diploma in Nursing Admission Test Question Solution 2023.pdf
Diploma in Nursing Admission Test Question Solution 2023.pdfDiploma in Nursing Admission Test Question Solution 2023.pdf
Diploma in Nursing Admission Test Question Solution 2023.pdfMohonDas
 
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdfP4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdfYu Kanazawa / Osaka University
 
In - Vivo and In - Vitro Correlation.pptx
In - Vivo and In - Vitro Correlation.pptxIn - Vivo and In - Vitro Correlation.pptx
In - Vivo and In - Vitro Correlation.pptxAditiChauhan701637
 
5 charts on South Africa as a source country for international student recrui...
5 charts on South Africa as a source country for international student recrui...5 charts on South Africa as a source country for international student recrui...
5 charts on South Africa as a source country for international student recrui...CaraSkikne1
 
The Stolen Bacillus by Herbert George Wells
The Stolen Bacillus by Herbert George WellsThe Stolen Bacillus by Herbert George Wells
The Stolen Bacillus by Herbert George WellsEugene Lysak
 
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptxSandy Millin
 
CAULIFLOWER BREEDING 1 Parmar pptx
CAULIFLOWER BREEDING 1 Parmar pptxCAULIFLOWER BREEDING 1 Parmar pptx
CAULIFLOWER BREEDING 1 Parmar pptxSaurabhParmar42
 
Philosophy of Education and Educational Philosophy
Philosophy of Education  and Educational PhilosophyPhilosophy of Education  and Educational Philosophy
Philosophy of Education and Educational PhilosophyShuvankar Madhu
 
The basics of sentences session 10pptx.pptx
The basics of sentences session 10pptx.pptxThe basics of sentences session 10pptx.pptx
The basics of sentences session 10pptx.pptxheathfieldcps1
 
HED Office Sohayok Exam Question Solution 2023.pdf
HED Office Sohayok Exam Question Solution 2023.pdfHED Office Sohayok Exam Question Solution 2023.pdf
HED Office Sohayok Exam Question Solution 2023.pdfMohonDas
 
Prescribed medication order and communication skills.pptx
Prescribed medication order and communication skills.pptxPrescribed medication order and communication skills.pptx
Prescribed medication order and communication skills.pptxraviapr7
 

Recently uploaded (20)

Easter in the USA presentation by Chloe.
Easter in the USA presentation by Chloe.Easter in the USA presentation by Chloe.
Easter in the USA presentation by Chloe.
 
How to Use api.constrains ( ) in Odoo 17
How to Use api.constrains ( ) in Odoo 17How to Use api.constrains ( ) in Odoo 17
How to Use api.constrains ( ) in Odoo 17
 
General views of Histopathology and step
General views of Histopathology and stepGeneral views of Histopathology and step
General views of Histopathology and step
 
Prelims of Kant get Marx 2.0: a general politics quiz
Prelims of Kant get Marx 2.0: a general politics quizPrelims of Kant get Marx 2.0: a general politics quiz
Prelims of Kant get Marx 2.0: a general politics quiz
 
Ultra structure and life cycle of Plasmodium.pptx
Ultra structure and life cycle of Plasmodium.pptxUltra structure and life cycle of Plasmodium.pptx
Ultra structure and life cycle of Plasmodium.pptx
 
What is the Future of QuickBooks DeskTop?
What is the Future of QuickBooks DeskTop?What is the Future of QuickBooks DeskTop?
What is the Future of QuickBooks DeskTop?
 
How to Make a Field read-only in Odoo 17
How to Make a Field read-only in Odoo 17How to Make a Field read-only in Odoo 17
How to Make a Field read-only in Odoo 17
 
Diploma in Nursing Admission Test Question Solution 2023.pdf
Diploma in Nursing Admission Test Question Solution 2023.pdfDiploma in Nursing Admission Test Question Solution 2023.pdf
Diploma in Nursing Admission Test Question Solution 2023.pdf
 
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdfP4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
 
Personal Resilience in Project Management 2 - TV Edit 1a.pdf
Personal Resilience in Project Management 2 - TV Edit 1a.pdfPersonal Resilience in Project Management 2 - TV Edit 1a.pdf
Personal Resilience in Project Management 2 - TV Edit 1a.pdf
 
In - Vivo and In - Vitro Correlation.pptx
In - Vivo and In - Vitro Correlation.pptxIn - Vivo and In - Vitro Correlation.pptx
In - Vivo and In - Vitro Correlation.pptx
 
5 charts on South Africa as a source country for international student recrui...
5 charts on South Africa as a source country for international student recrui...5 charts on South Africa as a source country for international student recrui...
5 charts on South Africa as a source country for international student recrui...
 
The Stolen Bacillus by Herbert George Wells
The Stolen Bacillus by Herbert George WellsThe Stolen Bacillus by Herbert George Wells
The Stolen Bacillus by Herbert George Wells
 
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx
 
CAULIFLOWER BREEDING 1 Parmar pptx
CAULIFLOWER BREEDING 1 Parmar pptxCAULIFLOWER BREEDING 1 Parmar pptx
CAULIFLOWER BREEDING 1 Parmar pptx
 
Philosophy of Education and Educational Philosophy
Philosophy of Education  and Educational PhilosophyPhilosophy of Education  and Educational Philosophy
Philosophy of Education and Educational Philosophy
 
The basics of sentences session 10pptx.pptx
The basics of sentences session 10pptx.pptxThe basics of sentences session 10pptx.pptx
The basics of sentences session 10pptx.pptx
 
HED Office Sohayok Exam Question Solution 2023.pdf
HED Office Sohayok Exam Question Solution 2023.pdfHED Office Sohayok Exam Question Solution 2023.pdf
HED Office Sohayok Exam Question Solution 2023.pdf
 
Finals of Kant get Marx 2.0 : a general politics quiz
Finals of Kant get Marx 2.0 : a general politics quizFinals of Kant get Marx 2.0 : a general politics quiz
Finals of Kant get Marx 2.0 : a general politics quiz
 
Prescribed medication order and communication skills.pptx
Prescribed medication order and communication skills.pptxPrescribed medication order and communication skills.pptx
Prescribed medication order and communication skills.pptx
 

6months industrial training in data mining,ludhiana

  • 2. INTRODUCTION  Motivation: Why data mining?  What is data mining?  Data Mining: On what kind of data?  Data mining functionality  Are all the patterns interesting?  Classification of data mining systems  Major issues in data mining
  • 3. MOTIVATION: “NECESSITY IS THE MOTHER OF INVENTION”  Data explosion problem  Automated data collection tools and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories  We are drowning in data, but starving for knowledge!  Solution: Data warehousing and data mining  Data warehousing and on-line analytical processing  Extraction of interesting knowledge (rules, regularities, patterns, constraints) from data in large databases
  • 4. EVOLUTION OF DATABASE TECHNOLOGY  1960s:  Data collection, database creation, IMS and network DBMS  1970s:  Relational data model, relational DBMS implementation  1980s:  RDBMS, advanced data models (extended-relational, OO, deductive, etc.) and application-oriented DBMS (spatial, scientific, engineering, etc.)  1990s—2000s:  Data mining and data warehousing, multimedia databases, and Web databases
  • 5. WHAT IS DATA MINING?  Data mining (knowledge discovery in databases):  Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases  Alternative names  Knowledge discovery(mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc.  What is not data mining?  (Deductive) query processing.  Expert systems or small ML/statistical programs
  • 6. WHY DATA MINING? — POTENTIAL APPLICATIONS  Database analysis and decision support  Market analysis and management  target marketing, customer relation management, market basket analysis, cross selling, market segmentation  Risk analysis and management  Forecasting, customer retention, improved underwriting, quality control, competitive analysis  Fraud detection and management  Other Applications  Text mining (news group, email, documents) and Web analysis.  Intelligent query answering
  • 7. MARKET ANALYSIS AND MANAGEMENT (1)  Where are the data sources for analysis?  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  Conversion of single to a joint bank account: marriage, etc.  Cross-market analysis  Associations/co-relations between product sales  Prediction based on the association information
  • 8. MARKET ANALYSIS AND MANAGEMENT (2)  Customer profiling  data mining can tell you what types of customers buy what products (clustering or classification)  Identifying customer requirements  identifying the best products for different customers  use prediction to find what factors will attract new customers  Provides summary information  various multidimensional summary reports  statistical summary information (data central tendency and variation)
  • 9. CORPORATE ANALYSIS AND RISK MANAGEMENT  Finance planning and asset evaluation  cash flow analysis and prediction  contingent claim analysis to evaluate assets  cross-sectional and time series analysis (financial-ratio, trend analysis, etc.)  Resource planning:  summarize and compare the resources and spending  Competition:  monitor competitors and market directions  group customers into classes and a class-based pricing procedure  set pricing strategy in a highly competitive market
  • 10. FRAUD DETECTION AND MANAGEMENT (1)  Applications  widely used in health care, retail, credit card services, telecommunications (phone card fraud), etc.  Approach  use historical data to build models of fraudulent behavior and use data mining to help identify similar instances  Examples  auto insurance: detect a group of people who stage accidents to collect on insurance  money laundering: detect suspicious money transactions (US Treasury's Financial Crimes Enforcement Network)  medical insurance: detect professional patients and ring of doctors and ring of references
  • 11. FRAUD DETECTION AND MANAGEMENT (2)  Detecting inappropriate medical treatment  Australian Health Insurance Commission identifies that in many cases blanket screening tests were requested (save Australian $1m/yr).  Detecting telephone fraud  Telephone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm.  British Telecom identified discrete groups of callers with frequent intra-group calls, especially mobile phones, and broke a multimillion dollar fraud.  Retail  Analysts estimate that 38% of retail shrink is due to dishonest employees.
  • 12. OTHER APPLICATIONS  Sports  IBM Advanced Scout analyzed NBA game statistics (shots blocked, assists, and fouls) to gain competitive advantage for New York Knicks and Miami Heat  Astronomy  JPL and the Palomar Observatory discovered 22 quasars with the help of data mining  Internet Web Surf-Aid  IBM Surf-Aid applies data mining algorithms to Web access logs for market-related pages to discover customer preference and behavior pages, analyzing effectiveness of Web marketing, improving Web site organization, etc.
  • 13. DATA MINING: A KDD PROCESS  Data mining: the core of knowledge discovery process. Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection Data Mining Pattern Evaluation
  • 14. STEPS OF A KDD PROCESS  Learning the application domain:  relevant prior knowledge and goals of application  Creating a target data set: data selection  Data cleaning and preprocessing: (may take 60% of effort!)  Data reduction and transformation:  Find useful features, dimensionality/variable reduction, invariant representation.  Choosing functions of data mining  summarization, classification, regression, association, clustering.  Choosing the mining algorithm(s)  Data mining: search for patterns of interest  Pattern evaluation and knowledge presentation  visualization, transformation, removing redundant patterns, etc.  Use of discovered knowledge
  • 15. DATA MINING AND BUSINESS INTELLIGENCE Increasing potential to support business decisions End User Business Analyst Data Analyst DBA Making Decisions Data Presentation Visualization Techniques Data Mining Information Discovery Data Exploration OLAP, MDA Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts Data Sources Paper, Files, Information Providers, Database Systems, OLTP
  • 16. ARCHITECTURE OF A TYPICAL DATA MINING SYSTEM Data Warehouse Data cleaning & data integration Filtering Databases Database or data warehouse server Data mining engine Pattern evaluation Graphical user interface Knowledge-base
  • 17. DATA MINING: ON WHAT KIND OF DATA?  Relational databases  Data warehouses  Transactional databases  Advanced DB and information repositories  Object-oriented and object-relational databases  Spatial databases  Time-series data and temporal data  Text databases and multimedia databases  Heterogeneous and legacy databases  WWW
  • 18. DATA MINING FUNCTIONALITIES (1)  Concept description: Characterization and discrimination  Generalize, summarize, and contrast data characteristics, e.g., dry vs. wet regions  Association (correlation and causality)  Multi-dimensional vs. single-dimensional association  age(X, “20..29”) ^ income(X, “20..29K”)  buys(X, “PC”) [support = 2%, confidence = 60%]  contains(T, “computer”)  contains(x, “software”) [1%, 75%]
  • 19. DATA MINING FUNCTIONALITIES (2)  Classification and Prediction  Finding models (functions) that describe and distinguish classes or concepts for future prediction  E.g., classify countries based on climate, or classify cars based on gas mileage  Presentation: decision-tree, classification rule, neural network  Prediction: Predict some unknown or missing numerical values  Cluster analysis  Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns  Clustering based on the principle: maximizing the intra-class similarity and minimizing the interclass similarity
  • 20. DATA MINING FUNCTIONALITIES (3)  Outlier analysis  Outlier: a data object that does not comply with the general behavior of the data  It can be considered as noise or exception but is quite useful in fraud detection, rare events analysis  Trend and evolution analysis  Trend and deviation: regression analysis  Sequential pattern mining, periodicity analysis  Similarity-based analysis  Other pattern-directed or statistical analyses
  • 21. ARE ALL THE “DISCOVERED” PATTERNS INTERESTING?  A data mining system/query 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., unexpectedness, novelty, actionability, etc.
  • 22. CAN WE FIND ALL AND ONLY INTERESTING PATTERNS?  Find all the interesting patterns: Completeness  Can a data mining system find all the interesting patterns?  Association vs. classification vs. clustering  Search for only interesting patterns: Optimization  Can a data mining system find only the interesting patterns?  Approaches  First general all the patterns and then filter out the uninteresting ones.  Generate only the interesting patterns—mining query optimization
  • 23. DATA MINING: CONFLUENCE OF MULTIPLE DISCIPLINES Data Mining Database Technology Statistics Other Disciplines Information Science Machine Learning Visualization