Java程序辅导

Data Warehousing 
and Data Mining
Lecture 1 Introduction
Wei Liu School of Computer 
Science and Software 
Engineering
Faculty of Engineering, 
Computing and 
Mathematics
CITS3401
CITS5504
Acknowledgement: The Lecture Slides are adapted from the original slides from Han’s textbook.
2Administrative
• Unit Coordinator & Lecturer 
– Dr. Wei Liu
• Email: wei.liu@uwa.edu.au
• Office: CSSE Room 2.18
• Phone: 64883095
• The Unit Materials are for both CITS3401 and CITS5504
– CITS3401 Bachelor of Science (Data Science Major)
– CITS5504 Master of Information Technology
• Common Lecture Hours:
– TUESDAYS 10:00 – 11:45am
3CITS3401 and CITS5504
• Common Consultation Hour:
– Tuesdays 2:00-3:00pm (Walk in - No appointment)
– Find me either in CSSE Room 2.18 or Lab 2.01
• Common Teaching Material
– Lecture slides, lab sheets and projects
• Different websites
– http://teaching.csse.uwa.edu.au/units/CITS3401
– http://teaching.csse.uwa.edu.au/units/CITS5504
• Different Lab Sessions (from Week 2 onward):
– CITS3401: Tuesdays 2:00-4:00pm Dr. Syed Mohammed Shamsul Islam 
(Shams)
– CITS5504: Mondays 9:00-11:00am Dr. Wei Liu
4Common Assessment Structures
• Two projects : 20% each
– An analysis of a business scenario through an OLAP tool. 
• We will be using an excel plug-in JEDOX for Data Warehousing Project.
– http://www.jedox.com/en/services/downloads
– An analysis of a data mining and exploration problem using WEKA. 
• Weka is a collection of machine learning algorithms for data mining tasks. 
The algorithms can either be applied directly to a dataset or called from your 
own Java Code
• http://www.cs.waikato.ac.nz/ml/weka/
• Mid-semester Test: 10% 
– at the lecture venue after the study break
• Final Examination: 50%
• Project Specifications and Instructions will be available on the 
course website.
5Text Book and Recommend Readings
• Course Text Book: 
– Data Mining: Concepts and Techniques
• 2nd ed., Jiawei Han and Micheline Kamber- 2006
• 3rd ed., Jiawei Han and Micheline Kamber, Jian Pei -2011
– Jiawei Han‘s web page:
• http://web.engr.illinois.edu/~hanj/
• References:
– Data Mining: Methods and Techniques by, A. Shawkat Ali and 
Saleh Wasimi Thomson, 2007
– Data Mining: The Textbook by, Charu C. Aggarwal, Springer, 
May 2015
6Introduction to Data Mining
• Why Data Mining?
• What Is Data Mining? A Knowledge Discovery (KDD) Process
• A Multi-Dimensional View of Data Mining/ classification
– What Kinds of Data Can Be Mined?
– What Kinds of Patterns Can Be Mined?
– What Kinds of Technologies Are Used?
– What Kinds of Applications Are Targeted? 
• Are all the patterns interesting?
• Integration of Data Mining System with Data Warehousing System
• Major Issues in Data Mining
7Why Data Mining?
• The Explosive Growth of Data: from terabytes to petabytes
– Data Explosion 
• Our capability of generating , collecting, storing and managing data has 
grown tremendously in the last 50 years.
– 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 and scalable analysis of massive data sets
8Potential Applications
• Data analysis and decision support
– Market analysis and management
• Target marketing, customer relationship management (CRM), 
market basket analysis, cross selling, market segmentation
– Risk analysis and management
• Forecasting, customer retention, improved underwriting, 
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
9Example 1: Market Analysis
• 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) 
• 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)
10
Example 2: 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
11
Example 3. Fraud Detection and 
Mining Unusual Patterns
• Approaches: Clustering & model construction for frauds, 
outlier analysis 
• Applications: Health care, retail, credit card service, telecomm.
– Money laundering: suspicious monetary transactions 
– Medical insurance: 
• Professional patients, ring of doctors, and ring of references 
• Unnecessary or correlated screening tests 
– Telecommunications: phone-call fraud 
• Phone call model: destination of the call, duration, time of day 
or week. Analyze patterns that deviate from an expected norm 
– Retail industry: 
• Analysts estimate that 38% of retail shrink is due to dishonest 
employees
• Anti-terrorism:
12
Evolution of Sciences
• Before 1600, empirical science
• 1600-1950s, theoretical science
– Each discipline has grown a theoretical component. Theoretical models often motivate 
experiments and generalize our understanding. 
• 1950s-1990s, computational science
– Over the last 50 years, most disciplines have grown a third, computational branch (e.g. 
empirical, theoretical, and computational ecology, or physics, or linguistics.)
– Computational Science traditionally meant simulation. It grew out of our inability to find 
closed-form solutions for complex mathematical models. 
• 1990-now, data science (data-driven science)
– The flood of data from new scientific instruments and simulations
– The ability to economically store and manage petabytes of data online
– The Internet and computing Grid that makes all these archives universally accessible 
– Scientific info. management, acquisition, organization, query, and visualization tasks 
scale almost linearly with data volumes.  Data mining is a major new challenge!
13
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.) 
– 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
14
Why Data Mining
Summary:
– Abundance of data and data archives are seldom visited.
– Far exceeded human ability for comprehension
– Intuitive decisions are prone to biases and errors, and is 
extremely time-consuming and costly
– Data mining tools perform data analysis and uncover important 
data patterns, contributing greatly to business strategies, 
knowledge bases, and scientific and medical research.
Data 
Tombs
Nuggets of 
knowledge
15
• 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?  (Knowledge Mining from data)
• Alternative names
– Knowledge discovery (mining) in databases (KDD), knowledge 
extraction, data/pattern analysis, data archeology, data dredging, 
information harvesting, business intelligence, etc.
• Watch out: Is everything “data mining”? 
– Simple search and query processing   
– (Deductive) expert systems
What is Data Mining?
16
What is Data Mining?
• Tremendous amount of data (terabyte-petabyte)
• High-dimensionality and high complexity of data
– Structured, un-structured, heterogeneous data
• Scalable
• Data mining involves integration of multiple disciplines: 
– Machine learning
– Pattern recognition
– Statistics
– Databases
– Business Intelligence
– Big data
• Efficient: Derived knowledge is new, interesting, informative and 
can be used for sophisticated application (decision making, 
process control, information management....)
17
Data Mining: Confluence of Multiple 
Disciplines
Data Mining
Database 
Technology Statistics
Machine
Learning
Pattern
Recognition
Algorithm
Other
Disciplines
Visualization
18
Steps of Knowledge Discovery 
(KDD) Process
• This is a view from typical 
database systems and data 
warehousing communities
• Data mining plays an essential 
role in the knowledge 
discovery process
Data Cleaning
Data Integration
Databases
Data Warehouse
Task-relevant Data
Selection
Data Mining
Pattern Evaluation
19
Data Warehousing and Mining 
Framework
20
KDD Process: Several Key Steps
• 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
21
Multi-Dimensional View of Data 
Mining
• Data to be mined
– Database data (extended-relational, object-oriented, 
heterogeneous, legacy), data warehouse, transactional data, 
stream, spatiotemporal, time-series, sequence, text and web, multi-
media, graphs & social and information networks
• Knowledge to be mined (or: Data mining functions)
– Characterization, discrimination, association, classification, 
clustering, trend/deviation, outlier analysis, etc.
– Descriptive vs. predictive data mining 
– Multiple/integrated functions and mining at multiple levels
• Techniques utilized (methodologies)
– Data-intensive, data warehouse (OLAP), machine learning, 
statistics, pattern recognition, visualization, high-performance, etc.
• Applications adapted
– Retail, telecommunication, banking, fraud analysis, bio-data mining, 
stock market analysis, text mining, Web mining, etc.
22
Data Mining: On What Kinds of 
Data?
• Structured and semi-structured data
– Relational database/ Object-relational data
– Data Warehouse, 
– Transactional Database
• Unstructured data 
– Data streams and sensor data
– Text data and web data
– Time-series data, temporal data, sequence data (incl. bio-
sequences) 
– Graphs, social networks and information networks
– Spatial data, spatiotemporal data and multimedia data
23
Relational Database
• A relational database is a collection of tables, each of which is 
assigned a unique name.
• Each table consists of a set of attributes (columns or fields) 
and usually stores a large set of tuples (records or rows).
• Each tuple in a relational table represents an object identified 
by unique key and described by a set of attribute values.
• A semantic data model, such as the entity relationship data 
model, is often constructed for relational databases.
• An ER data model represents the database as a set of entities 
and their relationships.
24
Relational Database
• Relational data can be accessed by database queries 
written in a relational language such as SQL.
• A given query is transformed into a set of relational 
operations such as join, selection and projection, 
and is then optimized for efficient processing.
• Efficiency of retrieval, efficiency of update and 
integrity are the key requirements of a good 
relational database.
25
An Example - AllElectronics
• Four relational tables: customer, item, employee and 
branch.
• Each relation consists of a set of attributes.
26
Example of Queries
• Show me a list of all items that were sold in the last 
quarter
• Show me the total sales of the last month, grouped 
by branch
• Which sales person has the highest amount of 
sales?
• How many sales transactions occurred in the month 
of September?
27
Purpose of relational databases
• The main purpose of a relational database is to store 
data correctly and retrieve data on demand.
• This type of data processing is sometime called 
Online Transaction Processing (OLTP).
• Relational databases are passive data repositories in 
the sense that a query only shows you what is 
stored in the database, but cannot tell you much 
about the meaning or trend of the data.
28
Data Warehouse of AllElectronics
• A data warehouse is a repository of information collected 
from multiple sources, stored under a unified schema, 
and that usually resides at a single site.
• Need is to provide an analysis of the company’s sales per 
item type per branch for the a specified period.
29
Data Warehouse
• The data warehouse 
may store a summary 
of the transactions per 
item type for each 
store or, summarized 
to a higher level, for 
each sales region.
30
Transactional Database
• A transactional database consists of a file where each 
record represents a transaction.
• Supports nested relation
• Transaction id: Items, Customer name, date…
• Sample Queries:
– Show me all the items purchased by ‘X’
– How many transactions include item number ‘Y’?
– market basket data analysis: Which items sold well 
together?  (Frequent item set)
31
Knowledge View: What Knowledge to be 
mined?
• Data summary in multidimensional space
– Data cube and OLAP (On-Line Analytical Processing)
• Pattern discovery
– Mining frequent patterns, association and correlation
– Applying pattern mining in many other tasks
• Classification and predictive modelling
– Model construction based on some training examples
– Prediction of new data based on constructed models
• Cluster analysis: How to group data to form new categories?
• Outlier analysis: Discovery of anomalies and rare events
• Trend and evolution analysis
32
Data Mining Function: (1) 
Characterization and Discrimination
• Data can be associated with classes or concepts. ( e.g., 
classes of items: computer, printers concept of 
customers: bigSpender, budgetSpender…  are the 
descriptions )
• Multidimensional concept description:
– Characterization: summarizing the class in general. (e.g. general 
specification of products whose sales increased by 10% and, 
….profile of customers who spend more than $1000 a year.  )
– Discrimination: comparison of target class with a contrast class.( 
compare the two groups of customers, such as who shop computer 
products regularly versus who rarely shop such products). Drilling 
down on dimensions such as occupation, age, etc.)
33
Data Mining Function: (2) 
Association and Correlation Analysis
• Frequent patterns (or frequent item_sets)
– What items are frequently purchased together ?
• Association, correlation vs. causality
– A typical association rule
• Milk  Bread [0.5%, 75%]  (support, confidence)
– Are strongly associated items also strongly correlated?
• How to mine such patterns and/or set rules efficiently in 
large datasets?  ( single or multi-dimensional 
association, minimum support threshold)
• How to use such patterns for classification, clustering, 
and other applications?
34
Data Mining Function: (3) 
Classification
• Classification and label prediction  
– Construct models (functions) based on some training examples or 
rules….[example: kind of response (good, mild, no) in sales 
campaign: price, brand, category, place_made…]
– 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, …
35
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
• Principle: Maximizing intra-class similarity & 
minimizing interclass similarity
• Example: homogeneous sub-population of 
AllElectronics customers (customer attributes: city, 
age, income,..)
• Many methods and applications
36
Data Mining Function: (5) Outlier 
Analysis
• Outlier analysis
– Outlier: A data object that does not comply with the general 
behavior of the data
– Most data mining methods discard outliers as noise or 
exceptions.
– Noise or exception? ― One person’s garbage could be 
another person’s treasure
– Methods: by product of clustering or regression analysis, 
distance analysis, statistical or probability model, 
– Useful in fraud detection, rare events are more interesting
– Example: By detecting a purchase of extremely large 
amount for a given account number.
37
Time and Ordering: Sequential 
Pattern, Trend and Evolution Analysis
• Sequence, trend and evolution analysis
– Trend, time-series, and deviation analysis: e.g., regression 
and value prediction
– Sequential pattern mining
• e.g., first buy digital camera, then buy large SD 
memory cards
– Periodicity analysis (e.g., overall stock market evolution 
regularities or for particular companies) 
– Motifs and biological sequence analysis
• Approximate and consecutive motifs
– Similarity-based analysis
• Mining data streams
– Ordered, time-varying, potentially infinite, data streams
38
Structure and Network Analysis
• Graph mining
– Finding frequent subgraphs (e.g., chemical compounds), trees 
(XML), substructures (web fragments)
• Information network analysis
– Social networks: actors (objects, nodes) and relationships (edges)
• e.g., author networks in CS, terrorist networks
– Multiple heterogeneous networks
• A person could be multiple information networks: friends, family, 
classmates, …
– Links carry a lot of semantic information: Link mining
• Web mining
– Web is a big information network: from PageRank to Google
– Analysis of Web information networks
• Web community discovery, opinion mining, usage mining, …
39
Methodology View: Confluence of 
Multiple Disciplines
Data Mining
Machine
Learning
Statistics
Applications
Algorithm
Pattern
Recognition
Distributed / 
cloud 
computing
Visualization
Database 
Technology
40
Why Confluence of Multiple 
Disciplines?
• Tremendous amount of data
– Algorithms must be scalable to handle big 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 and information networks
– Spatial, spatiotemporal, multimedia, text and Web data
– Software programs, scientific simulations
• New and sophisticated applications
41
Application View: Diverse Applications
• Mining text data and mining the Web 
– Web page classification and ranking, Weblog analysis, 
recommender systems, … 
• Mining business data 
– Transaction data, market basket analysis, fraud detection, 
… 
• Data mining and software/system engineering e.g., 
mining software bugs , optimize system performance, 
help in  computer vision
• Mining biological and medical data 
– Gene, protein, microarray data, biological networks 
• Mining social and information networks 
– Community discovery, information propagation, … 
• Invisible data mining : web search, stock market analysis
42
Classification of Data Mining System
• According to the kinds of database mined:
– relational, transactional, ….spatial, text, stream data….or World Wide Web
• According to the kinds of knowledge mined: 
– Based on mining functionalities, e.g. : characterization, discrimination, 
association,  ….can be multiple and/or integrated data mining…., can be 
distinguished based on granularity…, regular or irregular patterns(outliers) 
mining
• According to the techniques utilized: 
– degree of user interaction involved ( autonomous, interactive, query-driven), 
method of analysis (machine learning, pattern recognition, statistics, neural 
network….), combining merits of individual aspects..
• According to the applications adapted: 
– Finance, Telecommunication, DNA, stock-market…all purpose data mining 
system may not fit for domain specific minig.
43
Summary (till this)
• Data mining: Discovering interesting patterns and knowledge
from massive amount of data
• A natural evolution of science and information 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 data
• Data mining functionalities: characterization, discrimination, 
association, classification, clustering, trend and outlier 
analysis, etc.
• Data mining technologies and applications
44
Evaluation of Knowledge
• Are all mined knowledge interesting?
– One can mine tremendous amount of “patterns”  
– Some may fit only certain dimension space
• time, location, …
– Some may not be representative, may be transient, …
• Evaluation of mined knowledge → directly mine only 
interesting knowledge?
– Descriptive vs. predictive
– Coverage
– Typicality vs. novelty
– Accuracy
– Timeliness
– …
45
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., unexpectedness, 
novelty, actionability, etc.
46
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?
– Heuristic vs. exhaustive search
– 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 general all the patterns and then filter out the uninteresting 
ones
• Generate only the interesting patterns—mining query 
optimization
47
Integration of Data Mining and Data 
Warehousing
• Data mining systems, DBMS, Data warehouse systems coupling
– No coupling, loose-coupling, semi-tight-coupling, tight-coupling
• On-line analytical mining data
– integration of mining and OLAP technologies
• Interactive mining multi-level knowledge
– Necessity of mining knowledge and patterns at different levels of 
abstraction by drilling/rolling, pivoting, slicing/dicing, etc.
• Integration of multiple mining functions
– Characterized classification, first clustering and then association
48
Coupling Data Mining with DB/DW 
Systems
• No coupling—flat file processing for developing efficient and effective 
algorithms,… is a poor design as may spend time in preprocessing.
• Loose coupling- Fetching data from DB/DW.  Mining does not explore 
data structure and optimization methods provided by DB & DW.Difficult for 
high scalability.
• Semi-tight coupling—enhanced DM performance
– Provide efficient implement a few data mining primitives in a DB/DW 
system, e.g., sorting, indexing, aggregation, histogram analysis, multiway 
join, precomputation of some statistical functions
• Tight coupling—uniform processing environment
– DM is smoothly integrated into a DB/DW system, mining query is optimized 
based on mining query, indexing, query processing methods, etc.
49
Major Issues in Data Mining (1)
• Mining Methodology
– Mining various and new kinds of knowledge
– Mining knowledge in multi-dimensional space at multiple level of 
abstraction.
– Data mining: An interdisciplinary effort
– Boosting the power of discovery in a networked environment
– Handling noise, uncertainty, and incompleteness of data
– Pattern evaluation and pattern- or constraint-guided mining
• User Interaction
– Interactive mining
– Background knowledge (integrity constraints & deduction rules)
– Presentation and visualization of data mining results
50
Major Issues in Data Mining (2)
• Efficiency and Scalability
– Efficiency and scalability of data mining algorithms
– Parallel, distributed, stream, and incremental mining methods
• Diversity of data types
– Handling complex types of data
– Mining dynamic, networked, and global data repositories
• Data mining and society
– Social impacts of data mining
– Privacy-preserving data mining
– Invisible data mining
51
A Brief History of Data Mining Society
• 1989 IJCAI Workshop on Knowledge Discovery in Databases 
– Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 
1991)
• 1991-1994 Workshops on Knowledge Discovery in Databases
– Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. 
Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, 1996)
• 1995-1998 International Conferences on Knowledge Discovery in 
Databases and Data Mining (KDD’95-98)
– Journal of Data Mining and Knowledge Discovery (1997)
• ACM SIGKDD conferences since 1998 and SIGKDD Explorations
• More conferences on data mining
– PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM 
(2001), WSDM (2008), etc.
• ACM Transactions on KDD (2007)